project for B.TECH

1. MALLA REDDY ENGINEERING COLLEGE
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
A MAJOR PROJECT PRESENTATION
ON
DESIGN AND IMPLEMENTATION OF BILATERAL FILTER WITH EDGE
PRESEVING ALGORITHM FOR NOISY IMAGE RESTORATION
By Batch C4
M.Sindhu 20J41A04E7
M.Maddulety Yadav 20J41A04E5
B.Ankith Raj 20J41A04C7
S.Jagadeeswar Reddy 20J41A04H0
Under the guidance of
Dr. Vasudeva Bevara (Assistant Professor)

2. CONTENTS
1. Abstract
2. Objective
3. Literature survey
4. Introduction
5. Algorithm
6. Working principle
7. Block diagram
8. Applications
9. Results
10. Conclusion
11. Future scope
12. References
2

3. ABSTRACT
3
This project presents a novel approach for restoring noisy images using a bilateral filter
with an edge-preserving algorithm. The proposed method aims to effectively remove
noise while preserving important edges and details in the image. The bilateral filter is
utilized to smooth the image while retaining edge information, and the edge-preserving
algorithm further enhances the preservation of significant features. Experimental results
demonstrate the effectiveness of the proposed method in restoring noisy images,
achieving superior performance compared to existing techniques. The combination of
the bilateral filter and edge-preserving algorithm offers a promising solution for various
applications in image restoration and enhancement. We provide visual and quantitative
results on standard test images which show that this improvement is significant both
visually and in terms of PSNR and SSIM.

4. OBJECTIVE
• To effectively reduce noise and to get a better reconstructed image as output, so that its
suitable for many real-time applications.
• The objective of designing and implementing a bilateral filter with an edge-preserving
algorithm for noisy image restoration is to develop a robust method for enhancing images
corrupted by various types of noise while preserving important edges and structures.
4

5. LITERATURE REVIEW
S.NO AUTHOR NAME & YEAR METHOD USED ADVANTAGES DISADVANTAGES
1 A.K. Jain(1989) Median filtering Simple to implement and
much efficient
Remove image details
such as thin lines and
corners
2 Scott E Umbaugh(1998) Mean filtering reduces the intensity
variation between adjacent
pixels.
For small SNR’s it
break up image edges &
produce false noise
edges
3 S.Balasubramanian(2008) Non-linear Cascade Filtering Enhanced image quality Computational
complexity
4 Barwar Ferzo(2020) Wavelet based thresholding Automatically adopts to
different peak widths
Inefficient for
computing geometrical
features
5 Ademola E.
Ilesanmi(2021)
CNN(Deep learning) Extract image information
easily
Difficult to train image
5

6. 6
INTRODUCTION
IMAGE- It is a visual representation formed by group of pixels
NOISE-A random variation in an image
where y is the observed noisy image, x is the clean image, and n represents noise.
The purpose of noise reduction is to decrease the noise in natural images while
minimizing the loss of original features and improving the peak-signal-to-noise ratio
(PSNR).
The major things which should be considered while denoising are:
•flat areas should be smooth,
•edges should be protected without blurring,
•textures should be preserved etc.
y = x + n

7. • A bilateral filter is a non-linear and noise-reducing smoothing filter for images. It replaces the
intensity of each pixel with a weighted average of intensity values from nearby pixels. This
weight can be based on a Gaussian distribution.
• Unlike traditional linear filters that treat all pixels equally, bilateral filtering considers both
spatial and intensity information.
• Crucially, the weights depend not only on pixel distance (Euclidean distance) of pixels, but
also on the differences in pixel values (radiometric differences - range differences, such as
color intensity, depth distance, etc.). This preserves sharp edges.
• The filter combines a spatial Gaussian filter, which operates based on pixel distances, and an
intensity Gaussian filter, which considers differences in pixel values.
7
BILATERAL FILTER
EDGE PRESERVING
• Edge-preserving techniques are vital in image processing for maintaining sharp edges while
reducing noise or performing other modifications.
• These methods, like bilateral filtering, median filtering, and guided filtering, distinguish
between noise and actual edges, ensuring edge details remain intact during processing.
• This preservation is crucial in applications such as image denoising, enhancement, and edge
detection across various fields like medical imaging, satellite imaging, photography, and
computer vision.

8. BILATERAL FILTERING ALGORITHM
8
• Bilateral filtering works by considering both spatial distance and intensity
difference between pixels. For each pixel in the image, a weighted average is
computed using a Gaussian function to measure spatial distance and another
Gaussian function to measure intensity difference.

9. 9
EDGE PRESEVING ALGORITHM
R(i,j)=Median(f(i,j),b,d,e,g)

10. WORKING PRINCIPLE
• The design and implementation of a bilateral filter with an edge-preserving
algorithm for noisy image restoration follows a sophisticated process aimed at
achieving optimal noise reduction while preserving important image features.
• It works by applying a weighted average to each pixel in the image, taking into
account both its spatial distance from neighboring pixels and the intensity
• This approach ensures that smoothing occurs while maintaining sharp edges.
• Additionally, the incorporation of an edge-preserving algorithm further refines
the filtering process by identifying and preserving edges through the
adjustment of filtering parameters.
• Trade-offs exist between computational speed and denoising accuracy, with
applications in real-time image processing and resource-constrained
environments. Evaluation involves metrics like PSNR and SSIM, ensuring
robustness across different scenarios.
10

11. BLOCK DIAGRAM
11
Generate
texture map
Generate
block
discontinuity
map
Calculate
calculate
Bilateral Filter
Edge Preserving
Algorithm
Input
Image
Output Image

12. APPLICATIONS
12
• Image Processing
• Computer Vision
• Medical Imaging
• Industrial Inspection
• Smartphone and Digtal Cameras
• Photography

13. RESULTS
13

14. 14
PSNR COMPARISION

15. CONCLUSION
• In conclusion, the designed and implemented bilateral filter with an edge-preserving algorithm
presents a robust and effective approach for restoring noisy images.
• Through experimental validation, we have demonstrated its superiority over conventional
denoising methods in preserving important image features while effectively reducing noise, as
evidenced by higher PSNR and SSIM values.
15

16. FUTURE SCOPE
The future scope of approximate bilateral filtering for image denoising is promising, with
several potential avenues for advancement and application:
1. Deep Learning Integration: Combining traditional filtering methods with deep learning
models could enhance denoising performance.
2. Real-time Applications: Continued optimization of approximate bilateral filtering
algorithms could enable real-time image processing in fields like augmented reality and
medical imaging.
3. Adaptive Filtering: Research focuses on developing adaptive techniques to dynamically
adjust filter parameters based on image characteristics and noise levels.
4. Multi-modal Data Fusion: Extending bilateral filtering to handle multi-modal data could
enhance its utility in applications such as remote sensing and medical imaging.
5. Application in Emerging Technologies: Approximate bilateral filtering may find new
applications in computational photography, virtual reality, and digital entertainment for
enhancing image quality and user experience.
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17. REFERENCES
17
[1] P. K. R. Maddikunta, Q.-V. Pham, P. B, N. Deepa, K. Dev, “Industry 5.0: A Survey on
Enabling Technologies and Potential Applications”, Journal of Industrial Information
Integration, Vol.26, https://doi.org/10.1016/j.jii.2021.100257, n°3, pp. 1-31, Mar. 2022.
[2] J. Nakamura, Image Sensors and Signal Processing for Digital Still Cameras, New York,
NY, USA: Taylor & Francis, chap. 3, pp.55-90.
[3] D. H. Shin, R. H. Park, S. Yang, J. H. Jung, “Block-Based Noise Estimation Using
Adaptive Gaussian Filtering”, IEEE Transactions on Consumer Electronics, vol. 51, no. 1,
DOI: 10.1109/TCE.2005.1405723, pp. 218-226, Febr. 2005.
[4] C. Liu, W. T. Freeman, R. Szeliski, S. B. Kang, "Noise Estimation from a Single Image", in
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR),
DOI: 10.1109/CVPR.2006.207, pp. 901-908, June 2006.
[5] G. Chen, F. Zhu and P. A. Heng, "An Efficient Statistical Method for Image Noise Level
Estimation", in IEEE International Conference on Computer Vision (ICCV), DOI:
10.1109/ICCV.2015.62, pp. 477-485, Dec. 2015.