Framework For Kernel Based BM3D Algorithm

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Framework For Kernel Based BM3D Algorithm, Is A Well-Researched Topic, It Is To Be Used As A Guide Or Framework For Your Research

Abstract

Patch-based approaches such as Block Matching and 3D collaborative Filtering (BM3D) algorithm represent the current state-of-the-art in image denoising. However, BM3D still su ers from degradation in performance in smooth areas as well as loss of image details, speci cally in the presence of high noise levels. Integrating shape adaptive methods with BM3D improves the denoising outcome including the visual quality of the denoised image; and also maintains image details. In this study, we proposed a framework that produces multiple images using various
shapes. These images were aggregated at the pixel or patch levels for both stages in BM3D, and when appropriately aggregated, resulted in better denoising performance than BM3D by 1.15 dB, on average.
Keywords: Digital Image Processing, Image Restoration, Additive White Gaussian Noise, Image Denoising, Block Matching and 3D Filtering Algorithm (BM3D), Flat kernel, Adaptive Kernel, Adaptive Shape Algorithms.

Table of Contents

List of Figures V
List of Tables X
1 Introduction 1
1.1 Problem De nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 literature Review 5
2.1 Additive White Gaussian Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Measurements of Image Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 Mean Square Error (MSE) . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.2 Peak Signal to Noise (PSNR) . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.3 Structure Similarity Index Method (SSIM) . . . . . . . . . . . . . . . . . 8
2.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Image Denoising Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5 Spatial Domain Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5.1 Bilateral Filter (BF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5.2 A three stage integrated denoising approach for grey scale images . . . . 13
2.5.3 Non-local Means (NLM) . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5.4 Statistical Nearest Neighbors for Image Denoising . . . . . . . . . . . . . 16
2.5.5 Non-local Methods with Shape-Adaptive Patches (NLM-SAP) . . . . . . 19
2.6 Hybrid Domain Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6.1 Block-matching and 3D ltering (BM3D) . . . . . . . . . . . . . . . . . . 25

2.6.2 Image denoising with morphology-and size-adaptive block-matching transform
domain ltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6.3 Image denoising based on non-local means lter and its method noise
thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 Methodology 37
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Framework Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Results 55
4.1 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.1.1 Discussion on Numerical Results . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Visual Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.1 Discussion on Visual Results . . . . . . . . . . . . . . . . . . . . . . . . . 76
5 Conclusion and Future Work 77
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Bibliography 79

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Additional information

Author

Mena Abdelrahman Massoud

No of Chapters

5

No of Pages

97

Reference

YES

Format

PDF

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