Image Denoising by Multiscale - LMMSE in Wavelet Domain and Joint Bilateral Filter in Spatial Domain

 Abstract— This paper deals with LMMSE-based denoising scheme with a wavelet interscale model and Joint bilateral Filter in spatial domain. The proposed algorithm consists of two stages .In the first stage, a vector is represented by the wavelet coefficients at the same spatial locations at two adjacent scales and the LMMSE is applied to the vector. Compare to Orthogonal Wavelet Transform (OWT), Overcomplete Wavelet Expansion (OWE) provides better results hence it is employed. While applying the LMMSE rule, the important features in an image like edges, curves and textures can be identified. Also spatial domain method output provides a high quality denoising image than wavelet method with fewer artifacts; hence this wavelet domain output as a reference image for the Joint Bilateral Filter (JBF) .By using this reference image and the non-linear combination of information of adjacent pixel, the edge details of the images can be preserved in a well manner. The experimental results prove that the proposed approach is competitive when compared to other denoising methods in reducing various types of noise. Also the proposed algorithm outperforms other methods both visually and in case of objective quality

[1]  Ming Zhang,et al.  Multiresolution Bilateral Filtering for Image Denoising , 2008, IEEE Transactions on Image Processing.

[2]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Dennis M. Healy,et al.  Wavelet transform domain filters: a spatially selective noise filtration technique , 1994, IEEE Trans. Image Process..

[5]  Ioannis Pitas,et al.  Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.

[6]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 2000, IEEE Trans. Image Process..

[7]  J. Astola,et al.  Fundamentals of Nonlinear Digital Filtering , 1997 .

[8]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[9]  Lei Zhang,et al.  Multiscale LMMSE-based image denoising with optimal wavelet selection , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Thierry Blu,et al.  A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding , 2007, IEEE Transactions on Image Processing.

[11]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

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

[13]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[14]  Pierre Moulin,et al.  Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients , 2001, IEEE Trans. Image Process..

[15]  Kannan Ramchandran,et al.  Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[16]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[17]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[18]  K. Thomas Klasson Experimental Data Analysis , 1997 .

[19]  P. Yip,et al.  Discrete Cosine Transform: Algorithms, Advantages, Applications , 1990 .

[20]  M. Bronskill,et al.  Noise and filtration in magnetic resonance imaging. , 1985, Medical physics.

[21]  J. Canny A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Sudipta Roy,et al.  A NEW HYBRID IMAGE DENOISING METHOD , 2010 .

[23]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[24]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[25]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[26]  Haixian Wang,et al.  Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain , 2009, IEEE Transactions on Image Processing.

[27]  R. Bracewell Discrete Hartley transform , 1983 .

[28]  Aleksandra Pizurica,et al.  Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising , 2006, IEEE Transactions on Image Processing.

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

[30]  Lei Zhang,et al.  Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding , 2003, IEEE Trans. Medical Imaging.