Depth map denoising using collaborative graph wavelet shrinkage on connected image patches

In this paper, we propose a new patch-based image denoising algorithm using graph signal processing. The concept of this algorithm is to take advantage of the redundancy of the BM3D transform and the edge preservation property of graph-based image processing. More specifically, we collect similar patches in the image, and construct a graph by connecting obtained patches. Then we apply a graph wavelet filter bank on graph signals to attenuate additive white gaussian noise by shrinking derived coefficients. We apply our proposed algorithm to depth map denoising. The experimental results demonstrate significant performance gains for the edge preservation and the noise reduction.

[1]  Gerlind Plonka-Hoch,et al.  The Easy Path Wavelet Transform: A New Adaptive Wavelet Transform for Sparse Representation of Two-Dimensional Data , 2008, Multiscale Model. Simul..

[2]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[3]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

[4]  Pierre Vandergheynst,et al.  Wavelets on Graphs via Spectral Graph Theory , 2009, ArXiv.

[5]  Oscar C. Au,et al.  Depth map denoising using graph-based transform and group sparsity , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[6]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[7]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[8]  Zhihai He.  Peak Transform for Efficient Image Representation and Coding , 2007, IEEE Transactions on Image Processing.

[9]  Michael Elad,et al.  Image Processing Using Smooth Ordering of its Patches , 2012, IEEE Transactions on Image Processing.

[10]  Sunil K. Narang,et al.  Compact Support Biorthogonal Wavelet Filterbanks for Arbitrary Undirected Graphs , 2012, IEEE Transactions on Signal Processing.

[11]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

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

[13]  Yuichi Tanaka,et al.  Oversampled graph laplacian matrix for graph signals , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[14]  Yuichi Tanaka,et al.  M-Channel Oversampled Graph Filter Banks , 2014, IEEE Trans. Signal Process..

[15]  R. Coifman,et al.  Diffusion Wavelets , 2004 .

[16]  Mark Crovella,et al.  Graph wavelets for spatial traffic analysis , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[17]  G. Plonka.  The Easy Path Wavelet Transform: A New Adaptive Wavelet Transform for Sparse Representation of Two-Dimensional Data , 2009 .

[18]  Jaejoon Lee,et al.  Edge-adaptive transforms for efficient depth map coding , 2010, 28th Picture Coding Symposium.

[19]  Yuichi Tanaka,et al.  M-channel oversampled perfect reconstruction filter banks for graph signals , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Sunil K. Narang,et al.  Perfect Reconstruction Two-Channel Wavelet Filter Banks for Graph Structured Data , 2011, IEEE Transactions on Signal Processing.