Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Efficient VLSI Architectures for Image
Enhance...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Efficient VLSI Architectures for Image
Enhance...
Introduction
Motivation & Objectives
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Outline
1 Introduction
2 Motivation & Object...
Introduction
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Conclusions & Future Scope
Image Enhancement : An Introduction
Image pr...
Introduction
Motivation & Objectives
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Conclusions & Future Scope
Image enhancement : An Example
(a) Original ...
Introduction
Motivation & Objectives
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Conclusions & Future Scope
Challenges
Image enhancement algorithms have...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Choice of Implementations
General Purpose Pr...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Choice of Implementation Cont.,
Application ...
Introduction
Motivation & Objectives
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Conclusions & Future Scope
Motivation
The motivation behind this work i...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Objectives
Development of efficient VLSI archi...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Hardware Design Flow
M. C, Hanumantharaju, P...
Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementati...
Introduction
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AROF Architectures and Its FPGA Implementati...
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The AROF have been used in this ...
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Design of new algorithm a...
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The work presented throws open ...
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VLSI Image Processing Groups in Foreign
Univ...
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VLSI Image Processing Groups in Indian Unive...
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VLSI Image Processing Journals
Elsevier Jour...
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VLSI Image Processing Industries
M. C, Hanum...
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Image Processing Books
M. C, Hanumantharaju,...
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VLSI Signal Processing Books
M. C, Hanumanth...
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Mohd Firdaus Zakaria, Haidi Ibrahi...
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Ming Z. Zhanga,Ming-Jung Seowa, Li...
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D. J Jobson, Z. Rahman, and G. A W...
Ph.D Dissertation Defense Slides on Efficient VLSI Architectures for Image Enhancement Techniques
Ph.D Dissertation Defense Slides on Efficient VLSI Architectures for Image Enhancement Techniques
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Ph.D Dissertation Defense Slides on Efficient VLSI Architectures for Image Enhancement Techniques

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This upload consists of Ph.D dissertation defense slides of the research topic "Efficient VLSI Architectures for Image Enhancement Techniques" of Visvesvaraya Technological University, Belgaum, Karnataka, India.

For suggestions & comments mail me at :
mchanumantharaju@gmail.com
hanu2005@yahoo.com

or contact me at : 9742290764

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Transcript of "Ph.D Dissertation Defense Slides on Efficient VLSI Architectures for Image Enhancement Techniques"

  1. 1. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Efficient VLSI Architectures for Image Enhancement Techniques Ph. D Dissertation Defense M. C. Hanumantharaju - 1DS07MEN02 Research Scholar Dr. M. Ravishankar Research Advisor Department of Information Science and Engineering Dayananda Sagar College of Engineering, Bangalore-560078 March 7, 2014 M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 1/146
  2. 2. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Efficient VLSI Architectures for Image Enhancement Techniques Ph. D Dissertation Defense M. C. Hanumantharaju - 1DS07MEN02 Research Scholar Dr. M. Ravishankar Research Advisor Department of Information Science and Engineering Dayananda Sagar College of Engineering, Bangalore-560078 March 7, 2014 M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 2/146
  3. 3. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Outline 1 Introduction 2 Motivation & Objectives 3 Contributions AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture 4 Conclusions & Future Scope M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 3/146
  4. 4. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Image Enhancement : An Introduction Image processing 2-D signal processing Improves characteristics, properties and parameters Image enhancement Key step in image processing Modifies the attributes of an image Makes it appropriate for analysis, diagnosis, and display. Some of the image enhancement applications include Sharpening: improves car license plate number Contrast enhancement: medical image enhancement. Edge enhancement: enhances objects in aerial image. The realm of image enhancement wraps up Reconstruction & Restoration Filtering Segmentation Compression & Transmission M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 4/146
  5. 5. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Image enhancement : An Example (a) Original Image (b) Enhanced Image M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 5/146
  6. 6. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Challenges Image enhancement algorithms have numerous parame- ters to specify and that needs to be adjusted to obtain satisfactory results. Lack of integrated algorithms. Presently, image enhancement research demands better reconstruction of high quality images than possible with available researcher methods. Image enhancement algorithms depends on the input im- ages instead of adapting to its local features. Limited speed achieved in software implementation since image enhancement algorithms consists of large array of data. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 6/146
  7. 7. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Choice of Implementations General Purpose Processors (GPPs) Flexible. Technology limits the pro- cessing speed. Limited performance. Instruction sets are not suitable for fast processing of high resolution images. GPP instructions are se- quential and hence system throughput decreases. Digital Signal Processors (DSPs) Improvement over GPPs. Falls between GPPs and ASICs. Inadequate pipelining and parallel processing. Fixed architectures that limits the performance. Parallel operation is possi- ble with multiple DSPs. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 7/146
  8. 8. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Choice of Implementation Cont., Application Specific ICs (ASICs) Fast & efficient. Fixed circuit. Large time to market. High cost, except for large volume commercial appli- cations. No optimization. Field Programmable Gate Arrays (FPGAs) High throughput. Dynamically reconfig- urable. Massive pipelining and parallelism. Cost effective. Attractive choice for real- ization of DIP algorithms. Present Research Work uses FPGAs for Implementation M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 8/146
  9. 9. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Motivation The motivation behind this work is to bring out the fea- tures in the image that are not clearly visible owing to different illumination conditions. Current market demands better reconstruction of high quality images than is possible with currently available research outputs. Limitations of image enhancement schemes: difficult to tune parameters, deficit of integrated algorithms, lack of quantitative standard, dependence on inputs instead of adapting to local features. Software implementation : inadequate speed. Hardware implementation of image enhancement algo- rithm is in great demand for applications such as medical, forensic and surveillance etc. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 9/146
  10. 10. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Objectives Development of efficient VLSI architectures for image en- hancement algorithms. Design & simulate the algorithm using software approach (C or Matlab). Test the algorithm for images having different environ- mental conditions. Realize the algorithm using HDL (Verilog or VHDL). Verify both software & hardware implementation results. Evaluate the efficiency of the algorithm using performance metrics such as PSNR, contrast, luminance, IEF and wavelet energy etc. Compare proposed approach with other existing methods. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 10/146
  11. 11. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Hardware Design Flow M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 11/146
  12. 12. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter (AROF) Non-linear filter. AROF is a powerful technique for denoising an image cor- rupted by salt & pepper noise or impulse noise. Impulse noise is often introduced into digital images dur- ing image acquisition or Interference during transmission. AROF not only adapts filter output but also window size : iterative algorithm. AROF window expands : All Pixels within the current window are noisy or median itself is noisy. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 12/146
  13. 13. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter : Flow Chart M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 13/146
  14. 14. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Noisy Lena (90% Noise Level) and Restored Image Figure: First Image : Lena Image with High Noise Density (90% Salt & Pepper Noise) Second Image : Restored Lena Image using AROF. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 14/146
  15. 15. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Andreadis et al.1 proposed FPGA implementation of real- time adaptive image impulse noise suppression. However, the system slows down for highly corrupted images. An efficient hardware implementation of weighted median filter using cumulative histogram proposed by Fahmy et al.2 reduces impulse noise satisfactorily. However, hard- ware complexity is high for smaller window size. 1 I. Andreadis, G. Louverdis, ”Real-time Adaptive Image Impulse Noise Sup- pression”, IEEE Tran. on Instrumentation and Measurement, Vol. 53, Issue 3, pp. 798-806, 2004. 2 S. A Fahmy, P.Y.K. Cheung and W. Luk, ”Novel FPGA-based implementa- tion of median and weighted median filters for image processing”, International Conference on Field Programmable Logic and Applications, pp. 142-147, 2005. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 15/146
  16. 16. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont.., Meena et al.3 proposed a optimized architectures for rank Order filter. However, optimizations are done for sorting network with out considering noise levels in an image. Chih et al.4 proposed an efficient denoising architecture for impulse noise removal in images. Although, decision tree based approach used in this scheme is effective for hardware implementation, technique may not provide sat- isfactory results for images corrupted with high noise den- sity. 3 S. M Meena and K. Linganagouda, ”Implementation and Analysis of Opti- mized Architectures for Rank Order Filter”, Journal of Real Time Image Pro- cessing, Vol. 3, Issue 1-3, pp. 33-41, 2008. 4 Chih-Yuan Lien, Chien-Chuan Huang, Pei-Yin Chen and Yi-Fan Lin in IEEE Tran. on Computers, Vol. 62, No. 4, pp. 631-643, April 2013. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 16/146
  17. 17. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Advantages of Proposed Method The proposed AROF reduces impulse noise without degrading image information. The reconstructed images using AROF provides better visual quality than possible with Adaptive Median Filter (AMF). AROF consists of sliding window, sorting network, median selection etc. Therefore, the proposed AROF is best suited for FPGA implementation. AROF has better performance than the AMF when the noise density is moderate or high. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 17/146
  18. 18. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter : Illustration M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 18/146
  19. 19. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter : Illustration Cont.., M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 19/146
  20. 20. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter : Illustration Cont.., M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 20/146
  21. 21. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Rank Order Filter : Illustration Cont.., M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 21/146
  22. 22. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Top Level Module of AROF M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 22/146
  23. 23. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of AROF M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 23/146
  24. 24. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture 3 × 3 Sliding Window Example M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 24/146
  25. 25. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of 3 × 3 Sliding Window M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 25/146
  26. 26. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of 5 × 5 Sliding Window M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 26/146
  27. 27. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of 7 × 7 Sliding Window M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 27/146
  28. 28. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Noise Detection Unit and Sorting Network Modules (a) Impulse Noise Detector (b) Nine Element Sorting Module M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 28/146
  29. 29. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Details of Nine Element Sorter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 29/146
  30. 30. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Details of Nine Element Sorter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 30/146
  31. 31. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture of Compare & Swap Module (c) Top Architecture of Compare & Swap (d) Detailed Architecture of Com- pare & Swap M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 31/146
  32. 32. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Sorting Networks : Comparison for 9 Elements Bubble sort : 36 comparators, 14 parallel Operations. Bose Nelson sort : 27 comparators, 11 parallel operations. Hibbard sort : 27 comparators, 12 parallel operations. Bitonic sort5 : 28 comparators, 8 parallel operations. Batchers merge exchange sort6 : 27 comparators, 8 parallel operations. Optimal sort : 25 comparators, 8 parallel operations (proposed). 5 Zdenek Vasicek and Lukas Sekanina, ”Novel Hardware Implementation of Adaptive Median Filters”, in proceedings of 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems, pp. 1-6, April, 2008. 6 Baddar, Sherenaz W. Al-Haj, and Kenneth E. Batcher, ”The AKS Sorting Network,” Designing Sorting Networks, Springer New York, pp. 73-80, 2011. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 32/146
  33. 33. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab Simulation Results of AMF and AROF for ”Lena” Image Figure: First Row: Original Images with Noise Level 20%, 40%, & 60%, Second Row: AMF Reconstructed Images, Third Row: AROF Recontructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 33/146
  34. 34. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab Simulation Results of AMF and AROF for ”House” Image Figure: First Row: Original Images with Noise Level 20%, 40%, & 60%, Second Row: AMF Reconstructed Images, Third Row: AROF Recontructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 34/146
  35. 35. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture PSNR & IEF Computation The Peak Signal to Noise Ration (PSNR) & Image Enhancement Factor (IEF) are computed as follows: PSNR = 10 log10 2552 MSE MSE = 1 M×N M i=1 N j=1 ˆI(i, j) − I(i, j) 2 where E(x, y) is the enhanced gray element at position (x, y), I(x, y) is the original gray element at position (x, y) and, p and q denote the size of the gray image. IEF = M i=1 N j=1[n(i,j)−I(i,j)]2 M i=1 N j=1[f (i,j)−I(i,j)]2 where n(x, y) is the noisy image, I(x, y) is the original image and f(x, y) is the reconstructed image. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 35/146
  36. 36. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Quality Assessment using PSNR (dB) and IEF for Lena and House Image Figure: First Row: PSNR for Lena & House Image, Second Row: IEF for Lena & House Image M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 36/146
  37. 37. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture ModelSim Simulation Results : Validity of Input M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 37/146
  38. 38. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture ModelSim Simulation Results : AROF Pixel Output M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 38/146
  39. 39. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Diagram for Illustrating the Pipelining Operation of AROF System M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 39/146
  40. 40. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RTL View of the Top Module AROF System M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 40/146
  41. 41. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Zoomed View of the Top Module Figure: U1: Impulse Noise Detection, U2: Sliding Window, U3: Sorting Network, U4: Median Computation, U5: Delay Unit and U6: Output Selection M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 41/146
  42. 42. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture FPGA (XC5VLX50-1FF1153) Device Utilization M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 42/146
  43. 43. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Summary The timing summary for the AROF system as reported by Xilinx ISE tool is as follows: Speed Grade : -1 Minimum period: 4.392ns (Maximum Frequency: 227.668 MHz) Minimum input arrival time before clock: 1.154ns Maximum output required time after clock: 4.101ns Clock period: 4.392ns (frequency: 227.668MHz) Total number of paths / destination ports: 50736 / 5200 Delay: 4.392ns (Levels of Logic = 5) Total number of paths / destination ports: 8 / 8 Offset: 1.154ns (Levels of Logic = 1) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 43/146
  44. 44. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab & Hardware Results of ”Butterfly” Image Figure: First Row: Original Images with Noise Levels 20%, 40%, & 60%, Second Row: Matlab Reconstructed Images, Third Row: Hardware Recontructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 44/146
  45. 45. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab & Hardware Results of ”Traffic Signal” Image Figure: First Row: Original Images with Noise Levels 20%, 40%, & 60%, Second Row: Matlab Reconstructed Images, Third Row: Hardware Recontructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 45/146
  46. 46. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Quality Assessment using PSNR (dB) and IEF for Lena and House Image Figure: First Row: PSNR for ”Butterfly” & ”Traffic Signal” Image, Second Row: IEF for ”Butterfly” & ”Traffic Signal” Image M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 46/146
  47. 47. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Summary Table: Comparison of Present Implementation of AROF with Another Implementation Parameter Present Implementation Benkrid7 Picture Size (Pixels) 1600 × 1200 512 × 512 Frame Rate 118 25 7 K. Benkrid, D. Crookes, and A. Benkrid, ”Design and Implementation of a Novel Algorithm for General Purpose Median Filtering on FPGA’s”, In IEEE International Symposium on Circuits and Systems, (ISCAS2002), Vol. 4, pp. 425-428, 2002. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 47/146
  48. 48. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Publication Details An Efficient VLSI Architecture for Adaptive Rank Order Filter for Image Noise Removal, in the International Con- ference on Signal Acquisition & Processing (ICSAP2011), Singapore, 26-28 Feb, 2011. A Novel FPGA Implementation of Adaptive Rank Order Filter for Image Noise Removal, In an International Jour- nal of Computer and Electrical Engineering (IJCE), Vol. 4, No. 3, June 2012. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 48/146
  49. 49. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Adaptive Color Image Enhancement using GMF HSV color space is adapted since it separates color from intensity. The contrast of color image is enhanced by providing high frequency spatial information from the saturation compo- nent into luminance component. Local correlation is computed for luminance enhancement using Geometric Mean Filter (GMF). Saturation component is enhanced by stretching its dy- namic range. The hue component of HSV is preserved in order to avoid color distortion or shifting. Sobel operator is used in order to smooth edges. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 49/146
  50. 50. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Jayanta Mukherjee et al.8 proposed enhancement of color images by scaling DCT coefficients. Advantages Chromatic components are processed along with luminance. Visual quality is improved by reducing halo artifacts. Disadvantages This scheme violate gray world assumption. Improves the quality of an image at the cost of computational complexity. 8 Jayanta Mukherjee and Sanjit K. Mitra, ”Enhancement of Color Images by Scaling the DCT Coefficients,” IEEE Transactions on Image Processing, Vol. 17, No. 10, pp. 1783-1794, 2008. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 50/146
  51. 51. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Gang Song et al.9 proposed adaptive color image enhancement based on human visual properties in HSV space. Advantages Image enhancement is based on arithmetic mean & variance computation. Smooths local variations in an image. Noise is reduced to some extent. Disadvantages Results in blurring effect. Image details such as sharpness and edges are not satisfactory. 9 Gang Song and Xiang-Lei Qiao, ”Adaptive Color Image Enhancement based on Human Visual Properties,” in 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), pp. 1892-1895, 2008. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 51/146
  52. 52. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Tsai et al.10 proposed fast dynamic range compression with local contrast preservation algorithm. Advantages The contrast enhancement operation in this scheme is achieved by adaptive intensity transfer function and linear color remapping techniques. Processes 30 frames per second with the resolution of 640 × 480 pixels. Disadvantages However, there is still considerable room for optimization of the design based on Verilog coding since the author uses C++ for the design entry. 10 Chi-Yi Tsai, ”A Fast Dynamic Range Compression with Local Contrast Preservation Algorithm and its Application to Real-Time Video Enhancement,” IEEE Transactions on Multimedia, Vol. 14, Issue 4, pp. 1140-1152, 2012. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 52/146
  53. 53. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Ming et al.11 has developed high performance architecture for enhancement of the video stream captured under non-uniform lightning conditions. Advantages The RGB to HSV color space conversion adapted in this scheme use log-domain in order to avoid complex division process. Processes 30 frames per second with the resolution of 640 × 480 pixels. Disadvantages Color space conversion and image enhancement operation increases the latency of the system. The HSV to RGB converter modules developed to achieve reconstructed RGB images is not efficient from computation point of view. 11 M. Z Zhang, M. J Seow, and V. K Asari, ”A High Performance Architecture for Color Image Enhancement using a Machine Learning Approach,” in Interna- tional Journal of Computational Intelligence, Vol. 2, Issue 1, pp. 40-47, 2006. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 53/146
  54. 54. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Stefano Marsi et al.12 proposed illumination reflectance video enhancement based on FPGA implementation. Al- though, this scheme reduces halo artifacts, large sized fil- ter module, multiplier module and divider block exploited in this work increases the computation complexity. Faming et al.13 proposed a new pixel based variational model for remote sensing multi-source image fusion using gradient features. Visual inspection of the reconstructed image reveals distortion in the spectral information while merging the multi-spectral data. 12 Stefano Marsi and G. Ramponi, ”A Fexible FPGA Implementation for Illuminance-refectance video enhancement,” Journal of Real-Time Image Pro- cessing, pp. 1-13, 2011. 13 Guixu Zhanga Faming Fang, Fang Li and Chaomin Shen, ”A Variational Method for Multi-source Remote-Sensing Image Fusion,” in International Journal of Remote Sensing, Vol. 34, Issue 7, pp. 2470-2486, 2013. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 54/146
  55. 55. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Algorithm Steps Step 1 : Read the color image. Step 2 : Transform the image from RGB to HSV color space. Step 3 : Separate composite HSV into individual hue (H), saturation (S), and value (V) components. Step 4 : Enhance value and saturation components adaptively based on geometric mean filter. Step 5 : Preserve hue in order to avoid color distortion. Step 6 : Combine separated components of H, S and V into composite HSV. Step 7 : Transform back HSV to RGB color space. Step 8 : Display the enhanced color image. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 55/146
  56. 56. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Enhancement Equations Local Geometric Mean for Value or Luminance is given by : Vw = 1 mn [ i,j∈w V (i, j)] 1 mn where m, n represents window size, V is the luminance,Vw is the geometric mean for luminance. Local Geometric Mean for Saturation is given by : Sw = 1 mn [ i,j∈w S(i, j)] 1 mn where m, n represents window size, S is the saturation,Sw is the geometric mean for saturation. Local Variance for Value or Luminance is given by : σ2 v (x, y) = i,j∈w V (i, j) − Vw (x, y) 2 where x, y is the center pixel within the window. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 56/146
  57. 57. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Enhancement Equations Cont., Local Variance for Saturation is given by : σ2 s (x, y) = i,j∈w S(i, j) − Sw 2 Local Correlation Coefficient for luminance and saturation is given by : ρ(x, y) = i,j∈w [V (i,j)−Vw ][S(i,j)−Sw ]√ σ2 v (x,y)σ2 s (x,y) where ρ(x, y) is an adaptive measure for luminance enhancement. T1(x, y) = K1 V (x, y) − Vw (x, y) T2(x, y) = K2 S(x, y) − Sw (x, y) ρ(x, y) where K1 and K2 are constants which is assumed as 2. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 57/146
  58. 58. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Enhancement Equations Cont., New Luminance Enhancement with Saturation Feedback is given by : Venh(x, y) = V (x, y) + T1(x, y) − T2(x, y) where Venh(x, y) is the enhanced luminance. The saturation enhancement is given by : Senh(x, y) = [S(x, y)]γ where S(x, y) is the saturation component, γ is the stretch coefficient which determines the degree of saturation enhancement, Senh(x, y) is the enhanced saturation component. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 58/146
  59. 59. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Advantages of Proposed Method The proposed method has low artifacts or noise. Better contrast & Improved sharpness. Our method avoids color distortion since hue is preserved. Enhanced images are richer in color, have more clarity and better visual effects. Provides a general framework for ”Cross-Component Un- Sharp Masking (USM)”, which is a color enhancement strategy that may be applied to components from any color space. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 59/146
  60. 60. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Block Diagram of the Proposed AGMF Figure: Block Diagram of the Proposed Adaptive Color Image Enhancement Method based on Geometric Mean Filter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 60/146
  61. 61. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Flow Chart for RGB to HSV Color Space Conversion Figure: Flow Chart for RGB to HSV Color Space Conversion M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 61/146
  62. 62. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Flow Chart for HSV to RGB Color Space Conversion Figure: Flow Chart for HSV to RGB Color Space Conversion M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 62/146
  63. 63. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Top Architecture of RGB-HSV Color Space Conversion (a) Signal diagram for HSV to RGB Color Space Converter (b) Signal diagram for RGB to HSV Color Space Converter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 63/146
  64. 64. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture for RGB to HSV Color Space Converter Figure: Detailed Architecture for RGB to HSV Color Space M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 64/146
  65. 65. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture for HSV to RGB Color Space Converter Figure: Detailed Architecture for HSV to RGB Color Space M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 65/146
  66. 66. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture Details of AGMF Figure: Detailed Signal Diagram of Adaptive Color Image Enhancement based on Geometric Mean Filter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 66/146
  67. 67. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture for Geometric Mean Filter Module M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 67/146
  68. 68. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture for Geometric Mean Filter Module Cont., M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 68/146
  69. 69. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architecture for Histogram Equalization M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 69/146
  70. 70. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab Simulation Results of ”Tractor” Image Figure: (a) Original Image (b) Histogram Equalized Image (c) Image Enhanced using Gang et al. Method & (d) Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 70/146
  71. 71. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab Simulation Results of ”Office” Image Figure: (a) Original Image (b) Histogram Equalized Image (c) Image Enhanced using Gang et al. Method & (d) Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 71/146
  72. 72. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab Simulation Results of ”Girl” Image Figure: (a) Original Image (b) Histogram Equalized Image (c) Image Enhanced using Gang et al. Method & (d) Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 72/146
  73. 73. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Performance Comparison M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 73/146
  74. 74. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture ModelSim Simulation Results : Validity of RGB Input M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 74/146
  75. 75. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture ModelSim Simulation Results : Validity of the Enhanced Output M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 75/146
  76. 76. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RTL Top View of AGMF M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 76/146
  77. 77. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RTL Detailed View of RGB to HSV Color Space Converter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 77/146
  78. 78. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RTL Detailed View of HSV to RGB Color Space Converter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 78/146
  79. 79. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture FPGA Resource Utilization for RGB to HSV Converter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 79/146
  80. 80. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture FPGA Resource Utilization for HSV to RGB Converter M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 80/146
  81. 81. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture FPGA Resource Utilization for Adaptive Geometric Mean Filter based Color Image Enhancement M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 81/146
  82. 82. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Summary for RGB to HSV Conversion The timing summary for the RGB to HSV Color Space Converter as reported by Xilinx ISE tool is as follows: Speed Grade : -10 Minimum period: 29.730 ns (Maximum Frequency: 33.635 MHz) Minimum input arrival time before clock: 3.971 ns Maximum output required time after clock: 8.047 ns Clock period: 29.730 ns (frequency: 33.635 MHz) Total number of paths /destination ports: 158142043944387 / 824 Delay: 29.730 ns (Levels of Logic = 35) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 82/146
  83. 83. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Summary for HSV to RGB Conversion The timing summary for the HSV to RGB Color Space Converter as reported by Xilinx ISE tool is as follows: Speed Grade : -10 Minimum period: 7.815 ns (Maximum Frequency: 127.958 MHz) Minimum input arrival time before clock: 3.560 ns Maximum output required time after clock: 9.084 ns Clock period: 7.815 ns (frequency: 127.958 MHz) Total number of paths /destination ports: 4867 / 727 Delay: 7.815 ns (Levels of Logic = 18) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 83/146
  84. 84. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Summary for AGMF Design The timing summary for the AGMF Design as reported by Xilinx ISE tool is as follows: Speed Grade : -10 Minimum period: 4.286 ns (Maximum Frequency: 233.323 MHz) Minimum input arrival time before clock: 10.225 ns Maximum output required time after clock: 4.677 ns Clock period: 4.286 ns (frequency: 233.32 MHz) Total number of paths /destination ports: 187392 / 18456 Delay: 4.286 ns (Levels of Logic = 8) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 84/146
  85. 85. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RGB to HSV and vice versa: Software Approach Figure: First Row : Original Image, Image in HSV Space, Restored Image from HSV Space. Second Row :Original Image, Image in HSV Space, Restored Image from HSV Space. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 85/146
  86. 86. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RGB to HSV and vice versa: Hardware Approach Figure: First Row : Original Image, Image in HSV Space, Restored Image from HSV Space. Second Row :Original Image, Image in HSV Space, Restored Image from HSV Space. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 86/146
  87. 87. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab & Hardware Results of ”Nature” Image Figure: First Row: Original ”Nature” Image, Photoflair Software En- hanced Image (PSNR:28.12 dB), Image Enhanced using Histogram Equal- ization (PSNR:29.01 dB) Second Row: Matlab Reconstructed Image us- ing Proposed AGMF (PSNR:30.12 dB), Verilog Reconstructed Image using Proposed AGMF (PSNR:30.98 dB) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 87/146
  88. 88. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab & Hardware Results of ”Big Ben” Image Figure: First Row: Original ”Big Ben” Image, Photoflair Software En- hanced Image ((PSNR:27.68 dB), Image Enhanced using Histogram Equal- ization (PSNR: 28.3 dB) Second Row: Matlab Reconstructed Image us- ing Proposed AGMF (PSNR:29.07 dB), Verilog Reconstructed Image using Proposed AGMF (PSNR:29.16 dB) M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 88/146
  89. 89. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Conclusion Adaptive color image enhancement in HSV color space based on Geometric Mean Filter was presented. Luminance Enhancement is achieved using Saturation feed- back. Geometric mean filter offers better reconstructed image quality compared to arithmetic mean filter. Experimental results presented shows that the color im- ages enhanced by the proposed algorithm are clearer, more vivid and more brilliant. Performance of the proposed algorithm validated by ap- plying luminance, contrast and PSNR. The FPGA implementation of AGMF processes images of size 1600 × 1200 pixels at 121 frames per second. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 89/146
  90. 90. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Publication Details Adaptive Color Image Enhancement based on Geomet- ric Mean Filter, In Proceedings of International Confer- ence on Communication, Computing and Security (ICCCS 2011) NIT, Rourkela, India, Feb 12-14, ACM, 2011. A Novel Reconfigurable Architecture for Enhancing Color Image Based on Adaptive Saturation Feedback, In the In- ternational Conference on Advanced Information and Mo- bile Communication (AIM 2011), pp. 162-169, Nagpur, Maharashtra, India, April 21-22, Springer, 2011. A Novel FPGA Implementation of Adaptive Color Image Enhancement based on HSV Color Space, In the Inter- national Conference on Electronics and Computer Tech- nology (ICECT 2011), pp. 162-169, Kanyakumari, India, 08-10 April, IEEE, 2011. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 90/146
  91. 91. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Gaussian Image Enhancement : Introduction Retinex is a popular image enhancement method for bridg- ing the gap between images and the human observation of scenes. Retinex Algorithm was Proposed by Edwin Herbert Land in 1986. Proposed Gaussian based color image enhancement tech- nique is the modified version of NASA’s Retinex Algo- rithm Retinex is a model of lightness and color perception of human vision. Retinex is an adaptive imaging algorithm. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 91/146
  92. 92. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Gaussian Image enhancement : An Example Figure: Original Image and Enhanced Image using Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 92/146
  93. 93. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work D. J Jobson et al.14 proposed a color image enhancement based on multiscale retinex for bridging the gap between color images and Human observation of scenes. Advantages Good dynamic range compression and color constancy. Reconstructed images are favorable for Human visual per- ception and improve contrast. Disadvantages This method fails to produce good color rendition for a class of images that contain violations of the gray world assumption. Unable to remove Halo artifacts completely. Many parameters were assumed such as alpha, beta, gain, Gaussian scales etc. 14 D. J Jobson, Z. Rahman and G. A Woodell, ”A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observation of Scenes”, in IEEE Transactions on Image Processing, Vol. 6, pp. 965-976, 1997. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 93/146
  94. 94. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Digital Signal Processors (DSPs)15 has been used for the implementation of image enhancement algorithms. Advantages Improved efficiency compared to general purpose comput- ers. Disadvantages Only marginal improvement has been achieved since paral- lelism and pipelining incorporated in the design are inade- quate. This scheme uses optimized DSP libraries for complex op- erations and does not take full advantage of inherent par- allelism of image enhancement algorithm. The enhancement of 25-30 frames per second of large size video frames with 1024 × 768 pixel resolution is still not possible with DSPs. 15 D. J Jobson, G. D Hines, Z. Rahman and G. A Woodell, ”DSP Implemen- tation of the Retinex Image Enhancement Algorithm”, In Visual Information Processing XIII, Proc. SPIE, Vol. 5438, pp. 13-24, 2004. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 94/146
  95. 95. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., The neural network based learning algorithm16 provides an excellent solution for the color image enhancement with color restoration. Advantages The hardware implementation of the algorithm parallelizes the computation and delivers real time throughput for color image enhancement. Disadvantages The window related operations such as convolution, sum- mation and matrix dot products in an image enhancement architecture demands an tremendous amount of hardware resources. 16 M. Z Zhang, M. J Seow, and V. K Asari, ”A High Performance Architecture for Color Image Enhancement using a Machine Learning Approach”, In Interna- tional Journal of Computational Intelligence Research-Special Issue on Advances in Neural Networks, Vol 2, Issue 1, pp. 40-47, 2006. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 95/146
  96. 96. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Hiroshi Tsutsui et al.17 proposed an FPGA implementa- tion of adaptive real-time video image enhancement based on variational model of the Retinex theory. Authors claimed that the architectures developed in this scheme are efficient and can handle color picture of size 1900 × 1200 pixels at the real time video rate of 60 fps. However, the computational cost of the algorithm de- pends on the number of processing layers while the maxi- mum layers and iterations used are 5 and 30, respectively. Also, authors have not justified how high throughput has been achieved in spite of time consuming iterations to the tune of 30. 17 Hiroshi Tsutsui, H. Nakamura, R. Hashimoto, H. Okuhata, and T. Onoye, ”An FPGA Implementation of Real-time Retinex Video Image Enhancement”, In IEEE World Automation Congress (WAC), pp. 1-6. 2010. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 96/146
  97. 97. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Related Work Cont., Abdullah M. Alsuwailem et al.18 proposed a new approach for HE using FPGAs. Although efficient architectures were developed for HE, the reconstructed images using this scheme are generally not acceptable. The HE process loses the details such as edges, contrast and leads to over enhancement of noise in images. The enhancement approach using adaptive HE scheme also fails to produce satisfactory results since the process enlarges the contrast of background noise while lessening the exploitable signal. 18 Abdullah M. Alsuwailem and S. A Alshebeili, ”A New Approach for Real- time Histogram Equalization using FPGA”, In IEEE Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems (IS- PACS2005), pp. 397-400, 2005. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 97/146
  98. 98. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Gaussian Based Image Enhancement Method Original image (which is of poor quality and needing en- hancement) is read in RGB color space. The color components are separated followed by the con- volution with 3×3 Gaussian kernel in order to smooth the image. Logarithmic operation is accomplished in order to com- press the dynamic range of the image and to improve low intensity pixel values. Gain/Offset adjustment is done in order to translate the pixels into the display range of 0 to 255. The No. of scales, scales, gain and offset do not vary from one image to another. This implies that the algorithm is canonical. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 98/146
  99. 99. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Advantages of Proposed Method Depending on circumstances, the proposed method could achieve : Sharpening : Compensates for the blurring introduced by image formation process. Color constancy processing : Improves consistency of output as illumination changes. Good dynamic range compression and color rendition effect. Canonical constant : independent of inputs. General enhancement algorithm for all types of pictures. Provides satisfactory results for bi-modal pictures. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 99/146
  100. 100. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Flow Diagram of Proposed Enhancement Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 100/146
  101. 101. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Gaussian Kernels: 3 × 3 kernel and 5 × 5 kernel 1 16 1 2 1 2 4 2 1 2 1 1 273 1 4 7 4 1 4 16 26 16 4 7 26 41 26 7 4 16 26 16 4 1 4 7 4 1 Figure: Gaussian Kernels: 3 × 3 kernel and 5 × 5 kernel M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 101/146
  102. 102. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Enhancement Equations The two dimension (2D) Gaussian function is defined by g(x, y) = 1 2πσ2 e− x2+y2 2σ2 The Gaussian convolution matrix is given by G(x, y) = I(x, y) ⊗ g(x, y) Mathematically, 2D convolution can be represented as G(x, y) = M i=1 N j=1 I(i, j) × g(x − i, y − j) The convolution operation for a mask of 5 × 5 is given by P(x, y) = 4 i=0 Wi ×Pi 4 i=0 Wi M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 102/146
  103. 103. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Proposed Enhancement Equations Cont., The logarithmic processing on a 2D image is carried out by using the following Eqn. GL(x, y) = K × log2 [1 + G(x, y)] This gain/offset correction is accomplished by using Eqn. given below: I (x, y) = dmax GLmax −GLmin [GL(x, y) − GLmin] where dmax is the maximum intensity, which is chosen as, 255 for an image with 8-bit representation, GL(x, y) is the log-transformed image, GLmin is the minimum value of log transformed image, GLmax is the maximum value of log transformed image, I (x, y) is the enhanced image and, x and y represent spatial coordinates. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 103/146
  104. 104. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Block Diagram & Signal Description for the Top Module of Gaussian Based Image Enhancement System Block Diagram Signal Description Signals Description clk This is the global clock signal reset n Active low system reset rin [7:0] Red color component gin [7:0] Green color component bin [7:0] Blue color component ro [7:0] Enhanced red color component go [7:0] Enhanced Green color component bo [7:0] Enhanced Blue color component pixel valid Valid signal for enhanced RGB pixel M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 104/146
  105. 105. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of Gaussian Based Color Image Enhancement System M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 105/146
  106. 106. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Top Architecture of Serpentine Memory Signal Diagram Signal Description Signals Description clk Global clock signal reset n Active low system reset pixel in [7:0] R/G/B Input pixel window valid Valid signal for sliding window w11 [7:0] to w15 [7:0] First row pixel values w21 [7:0] to w25 [7:0] Second row pixel values w31 [7:0] to w35 [7:0] Third row pixel values w41 [7:0] to w45 [7:0] Fourth row pixel values w51 [7:0] to w55 [7:0] Fifth row pixel values M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 106/146
  107. 107. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of Serpentine Memory System M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 107/146
  108. 108. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Top Architecture of 2D Gaussian Convolution Signal Diagram Signal Description Signals Description window valid Valid signal from sliding window W11 to W15 First row pixel values W21 to W25 Second row pixel values W51 to W55 Fifth row pixel values G11 to G15 First row Gaussian kernel values G21 to G25 Second row Gaussian kernel values G51 to G55 Fifth row Gaussian kernel values conv out [7:0] Gaussian convolved output pixels conv valid Valid signal M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 108/146
  109. 109. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of 2D Gaussian Convolution Processor for R/G/B Color Channels M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 109/146
  110. 110. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Architectures of Adder, Multiplier and Logarithm Adder & Multiplier Module Logarithm Module M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 110/146
  111. 111. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of 24-bit Unsigned Adder M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 111/146
  112. 112. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Detailed Architecture of Pipelined Multiplier Design M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 112/146
  113. 113. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Results and Discussions Simulation Tool : Matlab Ver. 7.6.0.324 (R2008a). Resolution of Test Images : 640 × 480 Pixels (VGA), 800 × 600 Pixels (SVGA), 1024 × 768 Pixels (XGA). Test Images : http://dragon.larc.nasa.gov/retinex/pao/news/ http://visl.technion.ac.il/1999/99-07/www/ http://ivrg.epfl.ch/index.html Performance Metrics : Contrast Enhancement, Luminance Enhancement, Peak Signal to Noise Ratio (PSNR). Histogram : Plotted to show Pixel Distribution. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 113/146
  114. 114. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Comparison of Matlab Reconstructed Pictures Using Image Enhancement Algorithms: ”Trees” Image Figure: First Row:Original Image, Histogram Equalization, NASA’s MSRCR. Second Row:Chih et al. MSRCR, Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 114/146
  115. 115. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Comparison of Matlab Reconstructed Pictures Using Image Enhancement Algorithms: ”Palette” Image Figure: First Row:Original Image, Histogram Equalization, NASA’s MSRCR. Second Row:Chih et al. MSRCR, Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 115/146
  116. 116. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Comparison of Matlab Reconstructed Pictures Using Image Enhancement Algorithms: ”House” Image Figure: First Row:Original Image, Histogram Equalization, NASA’s MSRCR. Second Row:Chih et al. MSRCR, Proposed Method M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 116/146
  117. 117. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture ModelSim Simulation Waveforms for Inputting Image Data M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 117/146
  118. 118. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Waveforms of Sliding Window Module for one of the R/G/B Color Components M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 118/146
  119. 119. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Waveforms for Gaussian Convolution Output at 21090 ns M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 119/146
  120. 120. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Starting of Enhanced Pixel Data M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 120/146
  121. 121. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Waveforms for Ending of Reconstructed Pixels at 13,31,990 ns M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 121/146
  122. 122. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Processing Time Report for the Top Design Module of Gaussian Based Image Enhancement System Module Clock Cycles Required to Process each pixel data Sliding Window 1029 Gaussian convolution 23 Logarithm 1 gain/offset correction 7 M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 122/146
  123. 123. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Timing Diagram for Illustrating the Pipelining Operation of the Proposed System M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 123/146
  124. 124. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture RTL View of the ”Gaussian IE” RTL View of the Top Module aZoomed RTL View a Note: U1: Red Color,U2: Green Color, U3: Blue Color Component Pro- cessors M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 124/146
  125. 125. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Zoomed View of U1 or U2 or U3 Module M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 125/146
  126. 126. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture FPGA Resource Utilization for Gaussian Based Color Image Enhancement Design M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 126/146
  127. 127. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab and Verilog Reconstructed ”Tree”, ”House” and ”Color Palette” Images Figure: First Column: Original Images, Second Column: Matlab Reconstructed Images, Third Column: Hardware Reconstructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 127/146
  128. 128. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Matlab and Verilog Reconstructed ”Couple”, ”Dark Road” and ”Memorial Church” Images Figure: First Column: Original Images, Second Column: Matlab Reconstructed Images, Third Column: Hardware Reconstructed Images M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 128/146
  129. 129. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Performance Evaluation : Approximate Wavelet Energy Metric M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 129/146
  130. 130. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Performance Evaluation : Detailed Wavelet Energy Metric M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 130/146
  131. 131. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Conclusion Gaussian based color image enhancement algorithm was designed and architectures were developed. The proposed method is efficient from computation point of view as compared to other researcher methods. The visual quality of reconstructed pictures and image en- hancement achieved for various test images are compared using wavelet energy metric. Color image enhancement system is implemented on Xil- inx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution videos up to 1600 × 1200 pixels at 117 frames per second. RTL compliant Verilog coding of our system fits into a single FPGA chip with a gate count utilization of about 321,804. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 131/146
  132. 132. Introduction Motivation & Objectives Contributions Conclusions & Future Scope AROF Architectures and Its FPGA Implementation Adaptive Color Image Enhancement using GMF Gaussian Image Enhancement: Algorithm & Architecture Publication Details Design of Novel Algorithm and Architecture for Gaussian based Color Image Enhancement, in International Con- ference on Advances in Computing, Communication and Control, Mumbai, India, 18-19 Jan, 2013. Design and FPGA Implementation of a 2D Gaussian Sur- round Function with Reduced On-Chip Memory Utiliza- tion, in International Conference on Advances in Com- puting, Communication and Informatics (ICACCI2013), Mysore, India, 22-25 Aug, 2013. A Novel Full-Reference Color Image Quality Assessment Based on Energy Computation in the Wavelet Domain”, In Journal of Intelligent Systems, Vol. 22, No. 2, pp. 155-177, May 2013. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 132/146
  133. 133. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Conclusions The AROF have been used in this work to remove impulse noise since AROF has better filtering properties compared to AMF. The core modules of the AROF system, namely, sliding window, impulse noise detection, sorting network, median computation and output selection were realized using Ver- ilog for ASIC/FPGA implementation. A Novel algorithm for color image enhancement based on AGMF is proposed with the architecture development suitable for FPGA/ASIC implementations. The Verilog codes for the functional modules of AGMF, namely, RGB to HSV, histogram equalization, value com- ponent enhancement, HSV to RGB converter etc. have been developed and successfully simulated. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 133/146
  134. 134. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Conclusions Cont., Design of new algorithm and architectures for Gaussian based color image enhancement system for real-time ap- plications has been presented. The Gaussian color image enhancement functional mod- ules, namely, serpentine memory, 2D Gaussian convolu- tion, logarithm base-2 and gain/offset correction were re- alized using Verilog conforming to RTL coding guidelines practised in Industries. The designs presented exploits high degrees of pipelin- ing and parallel processing in order to achieve real time performance. Quality assessment of image enhancement algorithms are based metrics: CEP, LEP, PSNR, and WE etc. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 134/146
  135. 135. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Future Scope The work presented throws open a number of work that may be undertaken by researchers in the near future. The design of the proposed AROF is modular and flexible, and therefore, it can be upgraded to accommodate new modules, both present and future, without appreciable increase in hardware. The functional modules of AROF, AGMF & Gaussian im- age enhancement residing in FPGAs presently can be re- placed by ASIC resulting in more compact, low power, high speed and cost effective system suitable for volume production. A medical image enhancement technique based on retinex can also be designed and implemented on FPGA/ASIC by modifying the algorithms and architectures. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 135/146
  136. 136. Introduction Motivation & Objectives Contributions Conclusions & Future Scope VLSI Image Processing Groups in Foreign Universities Dr. Vijayan K. Asari, Old Dominion University, Norfolk, USA http://www.ece.odu.edu/~vasari/ Dr. Ryan Kastner, University of California, Sandiego http: //cseweb.ucsd.edu/~kastner/main Dr. Junguk Cho, University of California, Sandiego http:// cseweb.ucsd.edu/~j10cho/index.html Dr. Venkatesan Muthukumar, University of Nevada Las Vegas, USA http://www.ee.unlv.edu/~venkim/index.html Dr. Ming Z. Zhang, Old Dominion University, Norfolk, USA http://caprolibra.com/Prfdex.html Dr. Sudha Natarajan, NTU, Singapore http://www.ntu.edu. sg/home/sudha/ M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 136/146
  137. 137. Introduction Motivation & Objectives Contributions Conclusions & Future Scope VLSI Image Processing Groups in Indian Universities Dr. Swapna Banerjee, Dept. of EE, CAD and VLSI Laboratory, IIT Kharagpur, India. Dr. Nitin Chandrachoodan, Dept. of EE, VLSI Laboratory, IIT Madras, India. Dr. S. Srinivasan, VLSI Laboratory, Dept. of EE, IIT Madras, India. Dr. V. Kamakoti, Reconfigurable and Intelligent Systems Engi- neering Group (RISE Laboratory), Dept. of CSE, IIT Madras, India. Sanjay Sing, Scientist Fellow, IC Design Group, CEERI, Pilani, India. Dr. S. S. S. P Rao, Dept. of CSE, IIT Bombay, India. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 137/146
  138. 138. Introduction Motivation & Objectives Contributions Conclusions & Future Scope VLSI Image Processing Journals Elsevier Journal on Microprocessors and Micro-systems. Springer Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology. IEEE Transactions on Very Large Scale Integration (VLSI) Sys- tems. IEEE Transactions on Circuits and Systems for Video Technol- ogy. IEEE Journal on Computer Architectures for Intelligent Ma- chines. Journal of Circuits, Systems and Computers. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 138/146
  139. 139. Introduction Motivation & Objectives Contributions Conclusions & Future Scope VLSI Image Processing Industries M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 139/146
  140. 140. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Image Processing Books M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 140/146
  141. 141. Introduction Motivation & Objectives Contributions Conclusions & Future Scope VLSI Signal Processing Books M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 141/146
  142. 142. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Refrences Mohd Firdaus Zakaria, Haidi Ibrahim and Shahrel Azmin Suandi, ”A Review: Image Compensation Techniques”, Pro- ceedings of Second International Conference on Computer En- gineering and Technology (ICCET-2010), 16-18 April, 2010. C. Iakovidou, V. Vonikakis and I. Andreadis, ”FPGA implemen- tation of a real-time biologically inspired image enhancement algorithm”, Journal of Real Time Image Processing, Vol. 3, No. 4, pp. 269-287, 2008. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 142/146
  143. 143. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Refrences Ming Z. Zhanga,Ming-Jung Seowa, Li Tao and Vijayan K. Asari,”A tunable high-performance architecture for enhance- ment of stream video captured under non-uniform lighting con- ditions”, Journal of Micrprocessors and Microsystems, Vol. 32, Issue 7, pp. 386-393, 2008. Hiroshi Tsutsui, Hideyuki Nakamura, Ryoji Hashimoto, Hi- royuki Okuhata and Takao Onoye, ”An FPGA Implementation of Real-Time Retinex Video Image Enhancement”, Proceedings of World Automation Congress (WAC), pp. 1-6, 19-23 Sept, 2010. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 143/146
  144. 144. Introduction Motivation & Objectives Contributions Conclusions & Future Scope Refrences D. J Jobson, Z. Rahman, and G. A Woodell, A Multiscale retinex for bridging the gap between color images and the human ob- servation of scenes, IEEE Transaction Image Processing, Vol. 6, No. 7, pp. 965-976, July 1997. Xinghao Ding, Xinxin Wang, Quan Xiao, ”Color Image En- hancement with a Human Visual System based Adaptive Filter”, Proceedings of International Conference on Image Analysis and Signal Processing, April, 2010. Hongqing Hu and Guoqiang Ni, ”The improved algorithm for the defect of the Retinex Image Enhancement”, Proceedings of International Conference on Anti-Counterfeiting Security and Identification in Communication (ASID), pp. 257-260, July, 2010. M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 144/146
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