Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering

Noise reduction is a very important topic in image processing. We propose a new method to deal with the case where the noisy image has different noise levels in different regions. The main idea is to segment automatically the noisy image into several sub-images so that each sub-image has approximately the same noise level. We perform Block matching 3D filtering (BM3D) to these subimages in order to obtain denoised sub-images. We then merge sub-images together and enhance the discontinuous regions between the sub-images by performing BM3D again on small image patches. Our experimental results show the effectiveness of this proposed method in terms of Peak signal to noise ratio (PSNR) when compared with the bivariate wavelet shrinkage and the standard BM3D method. In addition to Gaussian white noise, our method performs better than the bivariate wavelet shrinkage and the standard BM3D method even for signal dependent noise.

[1]  Tien D. Bui,et al.  Translation-invariant denoising using multiwavelets , 1998, IEEE Trans. Signal Process..

[2]  Lu,et al.  Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising , 2014 .

[3]  Karen O. Egiazarian,et al.  Video denoising by sparse 3D transform-domain collaborative filtering , 2007, 2007 15th European Signal Processing Conference.

[4]  Anand Rangarajan,et al.  Image Denoising Using the Higher Order Singular Value Decomposition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jian,et al.  Image Denoising and Decomposition Using Non-convex Functional , 2012 .

[6]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[7]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[8]  A. Foi,et al.  Noise variance estimation in nonlocal transform domain , 2009, 2009 International Workshop on Local and Non-Local Approximation in Image Processing.

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

[10]  Muhammad Sharif,et al.  A Review of Image Denoising Methods , 2015 .

[11]  Ahmad Reza Naghsh-Nilchi,et al.  Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function , 2012, IEEE Transactions on Image Processing.

[12]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[13]  Nick G. Kingsbury,et al.  Image Denoising Using Derotated Complex Wavelet Coefficients , 2008, IEEE Transactions on Image Processing.

[14]  Tien D. Bui,et al.  Multivariate statistical modeling for image denoising using wavelet transforms , 2005, Signal Process. Image Commun..

[15]  Minh N. Do,et al.  Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations , 2013, IEEE Transactions on Biomedical Engineering.

[16]  Nikolay N. Ponomarenko,et al.  Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal , 2007, EURASIP J. Adv. Signal Process..

[17]  Zhang,et al.  Reaction-Diffusion Equation Based Image Denoising Algorithm , 2012 .

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

[19]  Giovanni Motta,et al.  The iDUDE Framework for Grayscale Image Denoising , 2011, IEEE Transactions on Image Processing.

[20]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[21]  Tien D. Bui,et al.  Image Denoising Based on Wavelet Shrinkage Using Neighbor and Level Dependency , 2009, Int. J. Wavelets Multiresolution Inf. Process..

[22]  Alessandro Foi,et al.  Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation , 2012, Electronic Imaging.

[23]  I. Selesnick,et al.  Bivariate shrinkage with local variance estimation , 2002, IEEE Signal Processing Letters.

[24]  A. Baudes,et al.  A Nonlocal Algorithm for Image Denoising , 2005, CVPR 2005.

[25]  ZHANG Wenge,et al.  Image Denoising Using Bandelets and Hidden Markov Tree Models ∗ , 2010 .

[26]  Guangyi Chen,et al.  Wavelet-based image denoising using three scales of dependency , 2012 .