A Novel Bayesian Patch-Based Approach for Image Denoising

Recently patch-based image denoising techniques have gained the attention of researchers as it is being used in numerous image denoising applications. This article is proposing a new Bayesian Patch-based image denoising algorithm using Quaternion Wavelet Transform (QWT) for grayscale images. In the proposed work, a patch model has been used instead of the Gibbs distribution based energy model. Experimental results indicate that the proposed algorithm effectively diminishes noise. The results of the developed approach are also compared with other efficient image denoising algorithms such as Expected Patch Log Likelihood (EPLL), Block-matching and 3D filtering (BM3D), Patch-Based Locally Optimal Wiener (PLOW), Weighted Nuclear Norm Minimization (WNNM), Hybrid Robust Bilateral Filter-Total Variation Filter (RBF-TVF) and Hybrid Total Variation Filter-Weighted Bilateral Filter (TVF-WBF) methods. The comparison revealed that the outcomes of the given approach are much sharper, clearer, and having the highest quality in comparison with other patch-based methods.

[1]  Wei Liu,et al.  Quaternion Wavelet Analysis and Application in Image Denoising , 2012 .

[2]  Mawardi Bahri.  Construction of Quaternion-Valued Wavelets , 2010 .

[3]  Peyman Milanfar,et al.  Patch-Based Near-Optimal Image Denoising , 2012, IEEE Transactions on Image Processing.

[4]  Florence Tupin,et al.  Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights , 2009, IEEE Transactions on Image Processing.

[5]  Han Tianfeng.  The Method of Quaternions Wavelet Image Denoising , 2014 .

[6]  Joonki Paik,et al.  Applications of multiscale transforms to image denoising: Survey , 2018, 2018 International Conference on Electronics, Information, and Communication (ICEIC).

[7]  David Zhang,et al.  Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Thomas Bülow,et al.  Hypercomplex spectral signal representations for the processing and analysis of images , 1999 .

[9]  Eduardo Bayro-Corrochano,et al.  The Theory and Use of the Quaternion Wavelet Transform , 2005, Journal of Mathematical Imaging and Vision.

[10]  Lei Zhang,et al.  Weighted Nuclear Norm Minimization with Application to Image Denoising , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Mahmoud R. El-Sakka,et al.  Adaptive Non-local Means Using Weight Thresholding , 2016, VISIGRAPP.

[12]  R. Vaillancourt,et al.  Two-dimensional quaternion Fourier transform of type II and quaternion wavelet transform , 2012, 2012 International Conference on Wavelet Analysis and Pattern Recognition.

[13]  M. Mitrea.  Clifford Wavelets, Singular Integrals, and Hardy Spaces , 1994 .

[14]  Xiaoqing Luo,et al.  Image Fusion Using Quaternion Wavelet Transform and Multiple Features , 2017, IEEE Access.

[15]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[16]  C. Stein,et al.  Estimation with Quadratic Loss , 1992 .

[17]  Rashid Ali,et al.  Fusion of Total Variation Filter and Weighted Bilateral Filter in Image Denoising , 2018 .

[18]  J. Polzehl,et al.  Propagation-Separation Approach for Local Likelihood Estimation , 2006 .

[19]  Ming Zhu,et al.  Multifocus color image fusion using quaternion wavelet transform , 2012, 2012 5th International Congress on Image and Signal Processing.

[20]  Yi Shen,et al.  Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain , 2012, J. Electronic Imaging.

[21]  Philippe Carré,et al.  Magnitude-phase of the dual-tree quaternionic wavelet transform for multispectral satellite image denoising , 2014, EURASIP J. Image Video Process..

[22]  Rui Lai,et al.  Improved non-local means filtering algorithm for image denoising , 2010, 2010 3rd International Congress on Image and Signal Processing.

[23]  Xia Liu,et al.  Image denoising based on quaternion wavelet Q-HMT model , 2014, Proceedings of the 33rd Chinese Control Conference.

[24]  Bo Yu,et al.  Continuous wavelet transforms on the space L2(R, H;dx) , 2004, Appl. Math. Lett..

[25]  Karsten Tabelow,et al.  Adaptive Smoothing of Digital Images: The R Package adimpro , 2007 .

[26]  Mohamed Cheriet,et al.  Markovian clustering for the non-local means image denoising , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[27]  Leonardo Traversoni.  Image Analysis Using Quaternion Wavelets , 2001 .

[28]  Yue Wu,et al.  James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising , 2012, IEEE Signal Processing Letters.

[29]  Yunfeng Peng,et al.  Combination of Total Variation and Robust Bilateral Filter in Image Denoising , 2018, ITITS.

[30]  Richard G. Baraniuk,et al.  Coherent Multiscale Image Processing Using Dual-Tree Quaternion Wavelets , 2008, IEEE Transactions on Image Processing.

[31]  Peng Geng,et al.  Adopting Quaternion Wavelet Transform to Fuse Multi-Modal Medical Images , 2017, Journal of Medical and Biological Engineering.

[32]  Yair Weiss,et al.  From learning models of natural image patches to whole image restoration , 2011, 2011 International Conference on Computer Vision.

[33]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).