This document presents a new image resolution enhancement technique using Undecimated Double Density Wavelet Transform (UDDWT). It begins with background on existing resolution enhancement methods and issues with discrete wavelet transform. It then describes the development of UDDWT and the proposed method which uses forward and inverse UDDWT to construct a high resolution image from a low resolution input image. Results show the technique improves measures like PSNR, VIF and BIQI compared to other methods, enhancing image quality. The technique offers exact shift invariance and preserves high frequency content better than interpolation methods.
Image Resolution Enhancement using DWT and Spatial Domain Interpolation Techn...IJERA Editor
Image Resolution is one of the important quality metrics of images. Images with high resolution are required in
many fields. In this paper, a new resolution enhancement technique is proposed based on the interpolation of
four sub band images generated by Discrete Wavelet Transform (DWT) and the original Low Resolution (LR)
input image. In this technique, the four sub band images generated by DWT and the input LR image are
interpolated with scaling factor, α and then performed inverse DWT to obtain the intermediate High Resolution
(HR) Image. The difference between the intermediate HR image and the interpolated LR input image is added
to the intermediate HR image to obtain final output HR Image. Lanczos interpolation is used in this technique.
The proposed technique is tested on well known bench mark images. The quantitative and visual results shows
the superiority of the proposed technique over the conventional and state of art image resolution enhancement
techniques in wavelet domain using haar wavelet filter.
This document discusses image fusion techniques for enhancing images. It begins with an introduction to image fusion, which combines relevant information from multiple images of the same scene into a single enhanced image. It then discusses discrete wavelet transform (DWT) based image fusion in more detail. Several image fusion rules for combining coefficient data during the DWT process are described, including maximum selection, weighted average, and window-based verification schemes. The importance of image fusion for applications like object identification, classification, and change detection is highlighted. Finally, the document reviews related work on different image fusion methods and algorithms proposed by other researchers.
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document presents an approach for image deblurring based on sparse representation and a regularized filter. The approach involves splitting the blurred input image into patches, estimating sparse coefficients for each patch, learning dictionaries from the coefficients, and merging the patches. The merged patches are subtracted from the blurred image to obtain the deblur kernel. Wiener deconvolution with the kernel is then applied and followed by a regularized filter to recover the original image without blurring. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM showed it performed better than existing methods, recovering images with more details and contrast.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Facial Recognition Method using Discrete Wavelet Transform Multiresolution Pyramid..........1
G. Preethi
Enhancing Energy Efficiency in WSN using Energy Potential and Energy Balancing Concepts ................. 9
Sheetalrani R. Kawale
DNS: Dynamic Network Selection Scheme for Vertical Handover in Heterogeneous Wireless Networks
.................................................................................................................................................................... 19
M. Deva Priya, D. Prithviraj and Dr. M. L Valarmathi
Implementation of Image based Flower Classification System................................................................ 35
Tanvi Kulkarni and Nilesh. J. Uke
A Survey on Knowledge Analytics of Text from Social Media .................................................................. 45
Dr. J. Akilandeswari and K. Rajalakshm
Progression of String Matching Practices in Web Mining – A Survey ..................................................... 62
Kaladevi A. C. and Nivetha S. M.
Virtualizing the Inter Communication of Clouds ...............................................................................72
Subho Roy Chowdhury, Sambit Kumar Patel, Ankita Vinod Mandekar and G. Usha Devi
Tracing the Adversaries using Packet Marking and Packet Logging ....................................................... 86
A. Santhosh and Dr. J. Senthil Kumar
An Improved Energy Efficient Clustering Algorithm for Non Availability of Spectrum in Cognitive Radio
Users ....................................................................................................................................................... 101
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Image Resolution Enhancement using DWT and Spatial Domain Interpolation Techn...IJERA Editor
Image Resolution is one of the important quality metrics of images. Images with high resolution are required in
many fields. In this paper, a new resolution enhancement technique is proposed based on the interpolation of
four sub band images generated by Discrete Wavelet Transform (DWT) and the original Low Resolution (LR)
input image. In this technique, the four sub band images generated by DWT and the input LR image are
interpolated with scaling factor, α and then performed inverse DWT to obtain the intermediate High Resolution
(HR) Image. The difference between the intermediate HR image and the interpolated LR input image is added
to the intermediate HR image to obtain final output HR Image. Lanczos interpolation is used in this technique.
The proposed technique is tested on well known bench mark images. The quantitative and visual results shows
the superiority of the proposed technique over the conventional and state of art image resolution enhancement
techniques in wavelet domain using haar wavelet filter.
This document discusses image fusion techniques for enhancing images. It begins with an introduction to image fusion, which combines relevant information from multiple images of the same scene into a single enhanced image. It then discusses discrete wavelet transform (DWT) based image fusion in more detail. Several image fusion rules for combining coefficient data during the DWT process are described, including maximum selection, weighted average, and window-based verification schemes. The importance of image fusion for applications like object identification, classification, and change detection is highlighted. Finally, the document reviews related work on different image fusion methods and algorithms proposed by other researchers.
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document presents an approach for image deblurring based on sparse representation and a regularized filter. The approach involves splitting the blurred input image into patches, estimating sparse coefficients for each patch, learning dictionaries from the coefficients, and merging the patches. The merged patches are subtracted from the blurred image to obtain the deblur kernel. Wiener deconvolution with the kernel is then applied and followed by a regularized filter to recover the original image without blurring. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM showed it performed better than existing methods, recovering images with more details and contrast.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Facial Recognition Method using Discrete Wavelet Transform Multiresolution Pyramid..........1
G. Preethi
Enhancing Energy Efficiency in WSN using Energy Potential and Energy Balancing Concepts ................. 9
Sheetalrani R. Kawale
DNS: Dynamic Network Selection Scheme for Vertical Handover in Heterogeneous Wireless Networks
.................................................................................................................................................................... 19
M. Deva Priya, D. Prithviraj and Dr. M. L Valarmathi
Implementation of Image based Flower Classification System................................................................ 35
Tanvi Kulkarni and Nilesh. J. Uke
A Survey on Knowledge Analytics of Text from Social Media .................................................................. 45
Dr. J. Akilandeswari and K. Rajalakshm
Progression of String Matching Practices in Web Mining – A Survey ..................................................... 62
Kaladevi A. C. and Nivetha S. M.
Virtualizing the Inter Communication of Clouds ...............................................................................72
Subho Roy Chowdhury, Sambit Kumar Patel, Ankita Vinod Mandekar and G. Usha Devi
Tracing the Adversaries using Packet Marking and Packet Logging ....................................................... 86
A. Santhosh and Dr. J. Senthil Kumar
An Improved Energy Efficient Clustering Algorithm for Non Availability of Spectrum in Cognitive Radio
Users ....................................................................................................................................................... 101
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents a redundant wavelet transform based method for single image super resolution. The proposed method decomposes a low resolution input image into subbands using redundant wavelet transform. The subbands are then interpolated using bicubic interpolation. Inverse redundant wavelet transform is applied to the interpolated subbands to generate the high resolution output image. The method is tested on various standard test images and wavelet types. Results show the proposed method achieves higher peak signal-to-noise ratios compared to conventional interpolation and discrete wavelet transform based super resolution methods.
A Comparative Study of Image Compression AlgorithmsIJORCS
The document compares three image compression algorithms: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and a hybrid DCT-DWT algorithm. DCT is used in JPEG and provides simple hardware implementation but can cause blocking artifacts at high compression. DWT provides multi-resolution decomposition and achieves higher compression ratios but requires more computation. The hybrid algorithm aims to combine the advantages of DCT and DWT by applying DWT followed by DCT, allowing for better performance than either individual method. Experimental results showed the hybrid approach generally had better performance in terms of PSNR, MSE, and compression ratio.
This document provides a summary of key concepts in digital image processing from two course units. It includes definitions of common terms like image, pixel, and color models. It also describes important techniques in image enhancement like spatial and frequency domain filtering. Key steps in digital image processing systems and transformations are outlined.
An efficient fusion based up sampling technique for restoration of spatially ...ijitjournal
The various up-sampling techniques available in the literature produce blurring artifacts in the upsampled,
high resolution images. In order to overcome this problem effectively, an image fusion based interpolation technique is proposed here to restore the high frequency information. The Discrete Cosine Transform interpolation technique preserves low frequency information whereas Discrete Sine Transform preserves high frequency information. Therefore, by fusing the DCT and DST based up-sampled images, more high frequency, relevant information of both the up-sampled images can be preserved in the restored,
fused image. The restoration of high frequency information lessens the degree of blurring in the fusedimage and hence improves its objective and subjective quality. Experimental result shows the proposed method achieves a Peak Signal to Noise Ratio (PSNR) improvement up to 0.9947dB than DCT interpolation and 2.8186dB than bicubic interpolation at 4:1 compression ratio.
This document outlines the course syllabus for Digital Image Processing (DIP). It includes 5 units covering key topics in DIP like digital image fundamentals, image enhancement, restoration and segmentation, wavelets and compression, and image representation and recognition. The syllabus allocates 45 class periods to cover these units in depth. Recommended textbooks and references for the course are also provided.
This document summarizes a research paper that proposes a new algorithm to detect copy-move forgery in digital images using Discrete Wavelet Transform (DWT). The algorithm works by applying DWT to decompose the input image into sub-bands, dividing the low-frequency sub-band into overlapping blocks, sorting the blocks lexicographically, identifying duplicate blocks based on their positions, and calculating shift vectors between matching blocks to detect the duplicated regions. The algorithm is more accurate and efficient than previous methods as it performs detection at the lowest DWT resolution to reduce computational complexity. Experimental results on test images show the algorithm can accurately detect copy-move forgeries with different sized duplicated regions.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This document compares the DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) image compression techniques. It finds that DWT provides higher compression ratios and avoids blocking artifacts compared to DCT. DWT allows for better localization in both spatial and frequency domains. It also has inherent scaling and better identifies visually relevant data, leading to higher compression ratios. However, DCT is faster than DWT. Experimental results on test images show that DWT achieves higher PSNR and lower MSE and BER than DCT, while providing a slightly higher compression ratio and completing compression more quickly.
This document summarizes a research paper on color image enhancement using an adaptive filter. It proposes a new algorithm that uses an adaptive filter to obtain the background image from a video based on color information. It then performs adaptive adjustment on the luminance image to get a locally enhanced image. Finally, it applies color restoration to obtain the enhanced color image. The algorithm aims to better preserve color information and reduce halo effects compared to techniques using discrete wavelet transforms. Experimental results show the adaptive filter produces clearer details and more natural colors in enhanced images and video frames.
The objective of this work is to propose an image
denoising technique and compare it with image denoising
using ridgelets. The proposed method uses slantlet transform
instead of wavelets in ridgelet transform. Experimental result
shows that the proposed method is more effective than ridgelets
in noise removal. The proposed method is effective in
compressing images while preserving edges.
Hybrid Digital Image Watermarking using Contourlet Transform (CT), DCT and SVDCSCJournals
Role of watermarking is dramatically enhanced due to the emerging technologies like IoT, Data analysis, and automation in many sectors of identity. Due to these many devices are connected through internet and networking and large amounts of data is generated and transmitted. Here security of the data is very much needed. The algorithm used for the watermarking is to be robust against various processes (attacks) such as filtering, compression and cropping etc. To increase the robustness, in the paper a hybrid algorithm is proposed by combining three transforms such as Contourlet, Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). Performance of Algorithm is evaluated by using similarity metrics such as NCC, MSE and PSNR.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document proposes an approach for image deblurring based on sparse representation and a regularized filter. The approach splits the blurred input image into patches, estimates sparse coefficients for each patch using dictionary learning, updates the dictionary, and estimates the deblur kernel. The deblur kernel is applied using Wiener deconvolution and further processed with a regularized filter to recover the original image. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM along with visual analysis showed it performed better deblurring compared to existing methods.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
40 9148 satellite image enhancement using dual edit tyasIAESIJEECS
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree M-Band Wavelet Transform (DTMBWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DTMBWT in this proposed enhancement technique. Inverse DTMBWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
Satellite Image Enhancement Using Dual Tree Complex Wavelet TransformjournalBEEI
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
This document discusses a proposed approach for multi-focus image fusion using a discrete cosine wavelet sharpness criterion. Multi-focus image fusion combines information from multiple images of the same scene to produce an "all-in-focus" image. The proposed approach uses a discrete cosine transform to calculate sharpness values for sub-blocks of the input images and selects the sharpest sub-blocks to include in the fused image. Experimental results on images of a clock, bottle, and book show the discrete cosine wavelet criterion produces fused images with higher quality than a bilateral gradient-based sharpness criterion, as measured by mutual information metrics.
Lifting Scheme Cores for Wavelet TransformDavid Bařina
The document presents research on improving the performance of wavelet transforms through lifting scheme cores. It introduces a lifting core as a processing unit that can continuously consume input and produce output while visiting each sample once in a cache-friendly manner. It discusses how lifting cores can handle borders, be configured for different processing orders, and allow reorganization of the underlying scheme for better parallelization and vectorization. The thesis aims to address shortcomings of prior methods through experimental evaluation of lifting cores on CPUs, GPUs, and FPGAs for 2D and 3D transforms as well as JPEG 2000 compression.
IRJET- Design of Image Resolution Enhancement by using DWT and SWTIRJET Journal
1) The document proposes a technique for image resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
2) It decomposes an input image using DWT into subbands, then applies bicubic interpolation to the high frequency subbands and SWT to minimize information loss.
3) The interpolated high frequency subbands are combined with the SWT high frequency subbands and input image. Inverse DWT is applied to generate a high resolution output image.
In Digital era sharing of images have become very
common and raises the risk of using it for unethical and
fraudulent purposes with the help of manipulation tools. Digital
image watermarking is one way to protect the digital information
(text, images, audio, and video) from fraudulent manipulations.
Digital Image Watermarking is a process of implanting data in
the original image for authentication. In this paper we are
providing one such watermarking scheme for color images. The
proposed method is designed to be robust for common attacks
with the aid of redundant discrete wavelet transform (RDWT)
and discrete cosine transform (DCT) properties. After applying
two levels RDWT decomposition to the blue channel of cover
image, we apply DCT to HH_LL subband i.e. 2nd level
decomposed coefficient of HH band and to the watermark.
Divided the HH_LL sub band into 4x4 subblocks and DCT
coefficients of the last subblock of the cover image are replaced
with the DCT coefficients of watermark. Inverse DCT and
inverse RDWT is performed to get watermarked image. The
performance of the proposed technique is measured using the
parameters PSNR and NCC.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
discrete wavelet transform based satellite image resolution enhancement muniswamy Paluru
The document discusses an image resolution enhancement technique using the discrete wavelet transform (DWT). It proposes interpolating the high-frequency subband images obtained from the DWT of the low-resolution input image. An intermediate stage is used to estimate the high-frequency subbands by utilizing the difference between the input image and its interpolated low-low (LL) subband. Inverse DWT is then applied to combine the interpolated images, generating the final enhanced image. The technique is compared to standard interpolation, wavelet zero padding, and state-of-the-art methods through qualitative and quantitative results. Matlab is identified as the required software for implementing the discrete wavelet transform and performing the proposed resolution enhancement algorithm.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
This document presents a redundant wavelet transform based method for single image super resolution. The proposed method decomposes a low resolution input image into subbands using redundant wavelet transform. The subbands are then interpolated using bicubic interpolation. Inverse redundant wavelet transform is applied to the interpolated subbands to generate the high resolution output image. The method is tested on various standard test images and wavelet types. Results show the proposed method achieves higher peak signal-to-noise ratios compared to conventional interpolation and discrete wavelet transform based super resolution methods.
A Comparative Study of Image Compression AlgorithmsIJORCS
The document compares three image compression algorithms: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and a hybrid DCT-DWT algorithm. DCT is used in JPEG and provides simple hardware implementation but can cause blocking artifacts at high compression. DWT provides multi-resolution decomposition and achieves higher compression ratios but requires more computation. The hybrid algorithm aims to combine the advantages of DCT and DWT by applying DWT followed by DCT, allowing for better performance than either individual method. Experimental results showed the hybrid approach generally had better performance in terms of PSNR, MSE, and compression ratio.
This document provides a summary of key concepts in digital image processing from two course units. It includes definitions of common terms like image, pixel, and color models. It also describes important techniques in image enhancement like spatial and frequency domain filtering. Key steps in digital image processing systems and transformations are outlined.
An efficient fusion based up sampling technique for restoration of spatially ...ijitjournal
The various up-sampling techniques available in the literature produce blurring artifacts in the upsampled,
high resolution images. In order to overcome this problem effectively, an image fusion based interpolation technique is proposed here to restore the high frequency information. The Discrete Cosine Transform interpolation technique preserves low frequency information whereas Discrete Sine Transform preserves high frequency information. Therefore, by fusing the DCT and DST based up-sampled images, more high frequency, relevant information of both the up-sampled images can be preserved in the restored,
fused image. The restoration of high frequency information lessens the degree of blurring in the fusedimage and hence improves its objective and subjective quality. Experimental result shows the proposed method achieves a Peak Signal to Noise Ratio (PSNR) improvement up to 0.9947dB than DCT interpolation and 2.8186dB than bicubic interpolation at 4:1 compression ratio.
This document outlines the course syllabus for Digital Image Processing (DIP). It includes 5 units covering key topics in DIP like digital image fundamentals, image enhancement, restoration and segmentation, wavelets and compression, and image representation and recognition. The syllabus allocates 45 class periods to cover these units in depth. Recommended textbooks and references for the course are also provided.
This document summarizes a research paper that proposes a new algorithm to detect copy-move forgery in digital images using Discrete Wavelet Transform (DWT). The algorithm works by applying DWT to decompose the input image into sub-bands, dividing the low-frequency sub-band into overlapping blocks, sorting the blocks lexicographically, identifying duplicate blocks based on their positions, and calculating shift vectors between matching blocks to detect the duplicated regions. The algorithm is more accurate and efficient than previous methods as it performs detection at the lowest DWT resolution to reduce computational complexity. Experimental results on test images show the algorithm can accurately detect copy-move forgeries with different sized duplicated regions.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This document compares the DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) image compression techniques. It finds that DWT provides higher compression ratios and avoids blocking artifacts compared to DCT. DWT allows for better localization in both spatial and frequency domains. It also has inherent scaling and better identifies visually relevant data, leading to higher compression ratios. However, DCT is faster than DWT. Experimental results on test images show that DWT achieves higher PSNR and lower MSE and BER than DCT, while providing a slightly higher compression ratio and completing compression more quickly.
This document summarizes a research paper on color image enhancement using an adaptive filter. It proposes a new algorithm that uses an adaptive filter to obtain the background image from a video based on color information. It then performs adaptive adjustment on the luminance image to get a locally enhanced image. Finally, it applies color restoration to obtain the enhanced color image. The algorithm aims to better preserve color information and reduce halo effects compared to techniques using discrete wavelet transforms. Experimental results show the adaptive filter produces clearer details and more natural colors in enhanced images and video frames.
The objective of this work is to propose an image
denoising technique and compare it with image denoising
using ridgelets. The proposed method uses slantlet transform
instead of wavelets in ridgelet transform. Experimental result
shows that the proposed method is more effective than ridgelets
in noise removal. The proposed method is effective in
compressing images while preserving edges.
Hybrid Digital Image Watermarking using Contourlet Transform (CT), DCT and SVDCSCJournals
Role of watermarking is dramatically enhanced due to the emerging technologies like IoT, Data analysis, and automation in many sectors of identity. Due to these many devices are connected through internet and networking and large amounts of data is generated and transmitted. Here security of the data is very much needed. The algorithm used for the watermarking is to be robust against various processes (attacks) such as filtering, compression and cropping etc. To increase the robustness, in the paper a hybrid algorithm is proposed by combining three transforms such as Contourlet, Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). Performance of Algorithm is evaluated by using similarity metrics such as NCC, MSE and PSNR.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document proposes an approach for image deblurring based on sparse representation and a regularized filter. The approach splits the blurred input image into patches, estimates sparse coefficients for each patch using dictionary learning, updates the dictionary, and estimates the deblur kernel. The deblur kernel is applied using Wiener deconvolution and further processed with a regularized filter to recover the original image. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM along with visual analysis showed it performed better deblurring compared to existing methods.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
40 9148 satellite image enhancement using dual edit tyasIAESIJEECS
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree M-Band Wavelet Transform (DTMBWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DTMBWT in this proposed enhancement technique. Inverse DTMBWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
Satellite Image Enhancement Using Dual Tree Complex Wavelet TransformjournalBEEI
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
This document discusses a proposed approach for multi-focus image fusion using a discrete cosine wavelet sharpness criterion. Multi-focus image fusion combines information from multiple images of the same scene to produce an "all-in-focus" image. The proposed approach uses a discrete cosine transform to calculate sharpness values for sub-blocks of the input images and selects the sharpest sub-blocks to include in the fused image. Experimental results on images of a clock, bottle, and book show the discrete cosine wavelet criterion produces fused images with higher quality than a bilateral gradient-based sharpness criterion, as measured by mutual information metrics.
Lifting Scheme Cores for Wavelet TransformDavid Bařina
The document presents research on improving the performance of wavelet transforms through lifting scheme cores. It introduces a lifting core as a processing unit that can continuously consume input and produce output while visiting each sample once in a cache-friendly manner. It discusses how lifting cores can handle borders, be configured for different processing orders, and allow reorganization of the underlying scheme for better parallelization and vectorization. The thesis aims to address shortcomings of prior methods through experimental evaluation of lifting cores on CPUs, GPUs, and FPGAs for 2D and 3D transforms as well as JPEG 2000 compression.
IRJET- Design of Image Resolution Enhancement by using DWT and SWTIRJET Journal
1) The document proposes a technique for image resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
2) It decomposes an input image using DWT into subbands, then applies bicubic interpolation to the high frequency subbands and SWT to minimize information loss.
3) The interpolated high frequency subbands are combined with the SWT high frequency subbands and input image. Inverse DWT is applied to generate a high resolution output image.
In Digital era sharing of images have become very
common and raises the risk of using it for unethical and
fraudulent purposes with the help of manipulation tools. Digital
image watermarking is one way to protect the digital information
(text, images, audio, and video) from fraudulent manipulations.
Digital Image Watermarking is a process of implanting data in
the original image for authentication. In this paper we are
providing one such watermarking scheme for color images. The
proposed method is designed to be robust for common attacks
with the aid of redundant discrete wavelet transform (RDWT)
and discrete cosine transform (DCT) properties. After applying
two levels RDWT decomposition to the blue channel of cover
image, we apply DCT to HH_LL subband i.e. 2nd level
decomposed coefficient of HH band and to the watermark.
Divided the HH_LL sub band into 4x4 subblocks and DCT
coefficients of the last subblock of the cover image are replaced
with the DCT coefficients of watermark. Inverse DCT and
inverse RDWT is performed to get watermarked image. The
performance of the proposed technique is measured using the
parameters PSNR and NCC.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
discrete wavelet transform based satellite image resolution enhancement muniswamy Paluru
The document discusses an image resolution enhancement technique using the discrete wavelet transform (DWT). It proposes interpolating the high-frequency subband images obtained from the DWT of the low-resolution input image. An intermediate stage is used to estimate the high-frequency subbands by utilizing the difference between the input image and its interpolated low-low (LL) subband. Inverse DWT is then applied to combine the interpolated images, generating the final enhanced image. The technique is compared to standard interpolation, wavelet zero padding, and state-of-the-art methods through qualitative and quantitative results. Matlab is identified as the required software for implementing the discrete wavelet transform and performing the proposed resolution enhancement algorithm.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
Image Resolution Enhancement by using Wavelet TransformIRJET Journal
This document presents a technique for enhancing the resolution of low resolution images using wavelet transforms. It decomposes low resolution images into sub-bands using discrete wavelet transform (DWT) and stationary wavelet transform (SWT). The high frequency sub-bands produced by DWT are interpolated and corrected using the high frequency sub-bands from SWT. An inverse DWT is then applied to combine the interpolated sub-bands and produce a higher resolution output image. The technique is compared to conventional methods like bilinear and bicubic interpolation as well as state-of-the-art resolution enhancement techniques. It is shown to produce higher quality results measured using metrics like peak signal-to-noise ratio. The technique has applications in
Performance analysis of Hybrid Transform, Hybrid Wavelet and Multi-Resolutio...IJMER
This document discusses and compares different methods for image data compression, including hybrid transform, hybrid wavelet transform, and multi-resolution hybrid wavelet transform. It first provides background on image compression techniques and reviews related work. It then proposes using Kekre transform and Hartley transform to generate a hybrid wavelet transform for image compression. The performance of this hybrid wavelet transform is analyzed and compared to a hybrid transform and multi-resolution hybrid wavelet transform in terms of root mean square error, mean absolute error, and average fractional change in pixel value across different compression ratios using various test images. The hybrid wavelet transform is found to provide lower error values than the other two methods.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
IRJET- Contrast Enhancement of Grey Level and Color Image using DWT and SVDIRJET Journal
The document presents a method for contrast enhancement of gray level and color images using discrete wavelet transform (DWT) and singular value decomposition (SVD). It begins with an introduction to common contrast enhancement techniques like general histogram equalization (GHE) and their limitations. The proposed method first applies GHE, then uses DWT to decompose the input image into subbands. It calculates a correction coefficient using the LL subbands and SVD. It multiplies this to the input image LL subband to generate a new LL subband. After recombining the subbands using inverse DWT, it yields an output image with enhanced contrast and brightness, without affecting color. Experimental results on sample images show improved mean, standard deviation and P
IRJET- Contrast Enhancement of Grey Level and Color Image using DWT and SVDIRJET Journal
The document presents a method for contrast enhancement of gray level and color images using discrete wavelet transform (DWT) and singular value decomposition (SVD). It begins with an introduction to common contrast enhancement techniques like general histogram equalization (GHE) and their limitations. The proposed method first applies GHE, then uses DWT to decompose the input image into subbands. It calculates a correction coefficient using the LL subbands and SVD. It multiplies this to the input image LL subband to generate a new LL subband. After recombining the subbands using inverse DWT, it produces an output image with enhanced contrast and brightness, without affecting color. Experimental results on sample images show improved mean, standard deviation and P
Image resolution enhancement by using wavelet transform 2IAEME Publication
This document discusses techniques for enhancing the resolution of digital images using wavelet transforms. It proposes a method that uses both stationary wavelet transform (SWT) and discrete wavelet transform (DWT) to decompose an input image into subbands, which are then interpolated using Lanczos interpolation before being combined via inverse DWT. The method is shown to achieve higher peak signal-to-noise ratios than traditional interpolation techniques like bilinear and bicubic interpolation as well as other wavelet-based super resolution methods, demonstrating its effectiveness for image resolution enhancement.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
1) The document describes an efficient region-based image retrieval system that uses discrete wavelet transform and k-means clustering. It segments images into regions, each characterized by features like size, mean, and covariance.
2) The system pre-processes images by resizing, converting to HSV color space, performing DWT, and using k-means clustering on DWT coefficients to generate regions. It extracts features for each region and stores them in a database.
3) For retrieval, it pre-processes the query image similarly and calculates similarities between the query regions and database regions based on their features, returning similar images.
4 ijaems jun-2015-5-hybrid algorithmic approach for medical image compression...INFOGAIN PUBLICATION
As medical imaging facilities move towards complete filmless imaging and also generate a large volume of image data through various advance medical modalities, the ability to store, share and transfer images on a cloud-based system is essential for maximizing efficiencies. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis.Wavelet transformation is widely used in the fields of image compression because they allow analysis of images at various levels of resolution and good characteristics. The algorithm what is discussed in this paper employs wavelet toolbox of MATLAB. Multilevel decomposition of the original image is performed by using Haar wavelet transform and then image is quantified and coded based on Huffman technique. The wavelet packet has been applied for reconstruction of the compressed image. The simulation results show that the algorithm has excellent effects in the image reconstruction and better compression ratio and also study shows that valuable in medical image compression on cloud platform.
4 ijaems jun-2015-5-hybrid algorithmic approach for medical image compression...INFOGAIN PUBLICATION
This document summarizes a research paper that proposes a hybrid algorithm for medical image compression using discrete wavelet transform (DWT) and Huffman coding techniques. The algorithm performs multilevel decomposition of medical images using DWT, quantizes the coefficients, assigns Huffman codes, and compresses the images. Simulation results on test medical images showed that the algorithm achieved excellent reconstruction quality with better compression ratios compared to other techniques. The algorithm is well-suited for compressing and transmitting large volumes of medical images over cloud platforms.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
This document summarizes a research paper that proposes a method to enhance the resolution of satellite images using discrete wavelet transform (DWT), interpolation, and inverse discrete wavelet transform (IDWT). Low resolution satellite images are decomposed into subbands using DWT. Bilinear interpolation is applied to each subband to increase resolution. IDWT is then used to combine the subbands into the enhanced, higher resolution output image. The method is tested on LANDSAT 8 images and evaluated using metrics like PSNR, MSE, and entropy. Results show the proposed method improves these metrics over other interpolation techniques, enhancing image quality and resolution.
Satellite image contrast enhancement using discrete wavelet transformHarishwar Reddy
This document discusses contrast enhancement of satellite images using discrete wavelet transform and singular value decomposition. It provides background on contrast and techniques like histogram equalization. It then describes discrete wavelet transform and singular value decomposition, their applications, advantages, and uses. The document concludes that a new technique was proposed combining DWT and SVD for image equalization, which showed better results than conventional techniques in experiments.
The document discusses an image resolution enhancement technique using discrete wavelet transforms. It involves decomposing a low resolution input image into sub-bands using stationary wavelet transforms and discrete wavelet transforms. Interpolation is then applied to all the low and high pass bands before taking the inverse discrete wavelet transform to produce a higher resolution output image. The technique aims to generate sharper images compared to traditional methods for applications like surveillance, medical imaging, and video standards conversion.
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Image Resolution Enhancement Using Undecimated Double Density Wavelet Transform
1. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 67
Image Resolution Enhancement Using Undecimated
Double Density Wavelet Transform
Varun P. Gopi varunpg@gecwyd.ac.in
Department of ECE
Government Engineering College Wayanad
Mananthavady, 670644, Kerala, India
V. Suresh Babu vsbsreeragam@gmail.com
Department of ECE
College of Engineering Trivandrum
Thiruvananthapuram, 690015, Kerala, India
Dilna C. dilnaeramam@gmail.com
Department of ECE
Government Engineering College Wayanad
Mananthavady, 670644, Kerala, India
Abstract
In this paper, an undecimated double density wavelet based image resolution enhancement
technique is proposed. The critically sampled discrete wavelet transform (DWT) suffers from the
drawbacks of being shift-variant and lacking the capacity to process directional information in
images. The double density wavelet transform (DDWT) is an approximately shift-invariant
transform capturing directional information. The undecimated double density wavelet transform
(UDDWT) is an improvement of the DDWT, making it exactly shift-invariant. The method uses a
forward and inverse UDDWT to construct a high resolution (HR) image from the given low
resolution (LR) image. The results are compared with state-of-the-art resolution enhancement
methods.
Keywords: Undecimated Double Density Wavelet Transform, Image Resolution, Stationary
Wavelet, Resolution Enhancement.
1. INTRODUCTION
Image resolution enhancement is a usable pre-process for many satellite image processing
applications, such as bridge recognition, building recognition and vehicle recognition. Image
resolution enhancement techniques can be categorized into two major types according to the
domain that they are applied in: 1) image domain and 2) transform domain. The techniques in the
image domain use the statistical and geometric data directly extracted from the input image itself
[1], [2], while transform-domain techniques use transformations such as decimated discrete
wavelet transform (DWT) to achieve the image resolution enhancement [3]-[6]. The decimated
DWT has been widely used for performing image resolution enhancement [3]-[5]. A common
assumption of DWT-based image resolution enhancement is that the low-resolution (LR) image is
the low pass filtered subband of the wavelet-transformed high-resolution (HR) image.
The image resolution is always a key feature for all kinds of images. With ever increasing size of
the displays need for super resolution images has also been increased. This is also impacted by
the limited size of the digital image sensor. Though widespread commercial cameras provide very
high resolution images, generally the scientific cameras still have the resolution of only 512 x 512.
Resolution enhancement has always been associated with the interpolation techniques.
2. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 68
Research suggests that interpolation methods increase the intensity of low frequency
components. This means the interpolated image will have less number of sharp intensity
transactions per pixel. A new method for resolution enhancement, which preserves high
frequency contents of the image is suggested in the paper. Spatial domain techniques lag in the
extraction and preservation of high frequency components of an image. This suggests that some
other technique not involving spatial domain is to be used. So the image needs to be transformed
to some other domain, processed and then converted back to the spatial domain. The domain
can be Fourier domain, wavelet domain or any other. Fourier domain is more suitable for spectral
filtering. The spectral filtering removes particular frequencies from the image. Wavelet domain
separates components of an image into individual matrices. These matrices can be processed
separately and combined together to get the desired result.
Fast algorithms for implementation of discrete wavelet transform have enhanced the use of the
wavelet domain for image resolution improvement. Different image processing algorithms can be
implemented with discrete wavelet transform (DWT). Double density wavelet transform (DDWT)
decomposes an image into nine sub bands. These sub bands are of half the dimensions of that of
image under consideration. Undecimated double density wavelet transform (UDDWT) is also
being used for the image resolution enhancement. UDDWT has nine sub bands similar to DDWT
but sub bands in UDDWT are of same size of that of the image. This paper proposes a new
method for image resolution enhancement based on UDDWT.
In this paper, we compared the proposed method with various conventional methods for image
resolution enhancement such as NEDI [1], HMM [7], DWT SR [8], DWT&SWT SR [9], LWT&SWT
[10]. This comparison of various measures on images shows the dominance of the proposed
method over existing methods.
2. DEVELOPMENT OF UNDECIMATED DOUBLE DENSITY WAVELET
TRANSFORM
Although the DWT [11, 12, 13] is a powerful signal processing tool, it has two severe
disadvantages:
1. Lack of shift-invariance, which means that minor shifts in the input signal, can cause major
variations in the distribution of energy between wavelet coefficients at different scales
2. Since the wavelet filters are separable and real, it causes poor directional selectivity for
diagonal features
The DWT is shift-variant because, the transform coefficients behave un-predictably under shifts of
input signal, a problem that has been treated by introducing large amounts of redundancy into the
transform to make it shift-invariant. The DWT has poor directional selectivity because it can only
differentiate three different spatial-feature orientations. The DDWT is almost shift-invariant, multi-
scale transform and has eight different spatial-feature orientations. Because the DDWT, at each
scale, has twice as many wavelets as the DWT, it achieves lower shift sensitivity than the DWT.
The undecimated Double Density Wavelet Transform follows the same filter bank structures of
DDWT except the up sampling/down sampling process. Here at any given level in the iterated
filter bank, this separable extension produces nine sub-bands in the same size as the original
image. To indicate the filters used along the row and column dimensions to create the nine sub
bands, the label of each of the sub-band is termed as , , ϵ {0,1,2}. The subscript
indicates filtering along the rows, while subscript denotes filtering along the columns. The
superscripts 0, 1, 2 indicate the particular filter , , used to filter along a
specified dimension to create the sub bands. Thus, at the end of the analysis filter bank, nine
sub-bands will be obtained as shown in Fig. 1. In the UDDWT synthesis filter bank, the
decomposed images are filtered using the filter coefficients (n), (n), (n). Fig. 2 illustrates
3. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 69
the synthesis filter bank structure, which composes the nine sub bands into a single image. In this
work, the image decomposition and reconstruction is done by using the filters designed by
Selesnick [14].
FIGURE 1: UDDWT analysis filter bank structure.
FIGURE 2: UDDWT synthesis filter bank structure.
4. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 70
3. PROPOSED METHOD
In the proposed method low resolution (LR) image is converted into a high resolution (HR) image
by using UDDWT. First, apply DDWT and undecimated double density wavelet transform
(UDDWT) on the input LR image. The DDWT produces 8 sub-bands which are decimated by
factor 2 and an LPF component. Then all 8 subbands are interpolated by factor of β. Similarly
UDDWT produces 8 sub-bands and an LPF component. Add corresponding subbands from each
of DDWT and UDDWT as shown in Fig. 3. It is known that in the wavelet domain, lowpass
filtering of the high resolution image produce the low resolution image. In other words, low
frequency subband is the low resolution of the original image. Therefore, instead of using low
frequency subband, which contains less information, the original image is used as the input to the
inverse UDDWT. The quality of the super resolved image increases when using the input image
instead of low frequency subband. Thus the output will be of higher resolution.
FIGURE 3: Proposed Method.
5. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 71
4. IMAGE QUALITY ANALYSIS
In this proposed work, the performance of the enhanced image is quantitatively analyzed by
means of three measures such as peak signal to noise ratio (PSNR), Blind Image Quality Index
(BIQI), and Visual Image Fidelity (VIF).
4.1. Peak Signal to Noise ratio (PSNR)
PSNR of the images is computed as
PSNR=10 ( )
Where is the peak pixel value in the image and usually is 255.
4.2. Blind Image Quality Index (BIQI)
BIQI [15] is also referred to as no reference image quality index. It refers to evaluating the quality
of an image without the need of reference image or any training images. Images with no blur and
noise offers higher BIQI value. Different parameters can be used to evaluate the quality of an
image blindly.
4.3. Visual Image Fidelity (VIF)
VIF index [16] is the ratio of distorted image information to reference image information which is
given by
Where is the quantity of information extracted from the test image and
is the quantity of information extracted from a reference image. The higher the VIF index, higher
the magnitude of test image. If VIF reaches to unity means that test image is perfect.
5. RESULTS AND ANALYSIS
The enhancing performance is tested using the images Lena, Elaine, Baboon, and Peppers and
all simulations were carried out in MATLAB. Fig. 4 and Fig. 5 illustrates the low resolution test
images and enhanced high resolution images respectively. In order to better perceive the
difference in enhancing, enlarged segments of the images of Fig. 4 & 5 are shown in Fig. 6. The
performance of the proposed method is analyzed by the variation of PSNR, BIQI Index, and VIF
index of different images. Table 1, Table 2, and Table 3 give the performance comparison of the
proposed and existing enhancing techniques in terms of PSNR, VIF, and BIQI index respectively.
The results indicate that the proposed technique is better than the other methods in enhancing.
In this study, an efficient method is proposed for image resolution enhancement. The essence of
the proposed work is the use of UDDWT for enhancement. The critically sampled discrete
wavelet transform (DWT) have the drawbacks of shift variance, aliasing and lack of directionality.
The DDWT is an improvement upon the critically sampled DWT and also nearly shift-invariant
transform capturing directional information. Although the DDWT utilizes more wavelets, some
lack a dominant spatial orientation, which prevents them from being able to isolate those
directions. The UDDWT making the image exactly shift-invariant. The proposed method
constructs a high resolution (HR) image from the given low resolution (LR) image by using
forward and inverse UDDWT. The edge enhanced by using UDDWT. Future work may include
enhancement of brightness by using Singular Value Decomposition (SVD).
6. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 72
Methods/ Images Lena Elaine Baboon Peppers
Bilinear 26.34 25.38 20.51 25.16
Bicubic 26.86 28.93 20.61 25.66
NEDI [1] 28.81 29.97 21.18 28.52
HMM [7] 28.86 30.51 21.47 29.58
DWT SR [8] 34.79 32.73 23.29 32.19
DWT & SWT SR [9] 34.82 35.01 23.87 33.06
SWT & LWT [10] 34.91 34.95 28.92 36.10
Proposed 40.32 42.78 34.06 39.76
TABLE 1: Comparison of PSNR in dB.
FIGURE 4: Low resolution test images.
7. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 73
Methods/ Images Lena Elaine Baboon Peppers
DWT & SWT SR [9] 0.14 0.19 0.17 0.19
SWT & LWT [10] 0.57 0.72 0.49 0.83
Proposed 0.67 0.71 0.51 0.85
TABLE 2: Comparison of VIF
Methods/ Images Lena Elaine Baboon Peppers
DWT & SWT SR [9] 28.21 34.02 46.09 34.37
SWT & LWT [10] 48.67 49.06 55.67 39.65
Proposed 51.24 52.85 58.43 43.42
TABLE 3: Comparison of BIQI.
FIGURE 5: Resolution Enhanced images.
8. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 74
FIGURE 6: Enlarged segments of the images of Fig. 4 & 5
9. Varun P. Gopi, V. Suresh Babu & Dilna C.
Signal Processing: An International Journal (SPIJ), Volume (8) : Issue (5) : 2014 75
6. CONCLUSION
The proposed method describes a new technique in image resolution enhancement. The
technique enhances the resolution of the image by using Undecimated Discrete Wavelet
Transform. The method uses a forward and inverse UDDWT to construct a high-resolution (HR)
image from the given LR image. UDDWT also has nine sub bands similar to DDWT but sub
bands in UDDWT are of same size of that of the image. The High Resolution image is
reconstructed from the LR image using the inverse DDWT. This method is tested by using four
well known images. The performance comparison shows that the proposed method is better than
other enhancement methods.
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