Multiscale Image Blind Denoising

Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown. End users only dispose the result of a complex image processing chain effectuated by uncontrolled hardware and software (and sometimes by chemical means). For such images, recent progress in noise estimation permits to estimate from a single image a noise model, which is simultaneously signal and frequency dependent. We propose here a multiscale denoising algorithm adapted to this broad noise model. This leads to a blind denoising algorithm which we demonstrate on real JPEG images and on scans of old photographs for which the formation model is unknown. The consistency of this algorithm is also verified on simulated distorted images. This algorithm is finally compared with the unique state of the art previous blind denoising method.

[1]  F. J. Anscombe,et al.  THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA , 1948 .

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

[3]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[4]  Leonid P. Yaroslavsky,et al.  Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window , 1996, Optics & Photonics.

[5]  Jaakko Astola,et al.  Transform domain image restoration methods: review, comparison, and interpretation , 2001, IS&T/SPIE Electronic Imaging.

[6]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2002, IEEE Trans. Image Process..

[7]  J. Portilla.  BLIND NON-WHITE NOISE REMOVAL IN IMAGES USING GAUSSIAN SCALE MIXTURES IN THE WAVELET DOMAIN , 2004 .

[8]  Javier Portilla,et al.  Full blind denoising through noise covariance estimation using Gaussian scale mixtures in the wavelet domain , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[9]  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).

[10]  Tamer F. Rabie,et al.  Robust estimation approach for blind denoising , 2005, IEEE Transactions on Image Processing.

[11]  Gabriel Cristóbal,et al.  The generalized Rényi image entropy as a noise indicator , 2007, SPIE International Symposium on Fluctuations and Noise.

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

[13]  Richard Szeliski,et al.  Automatic Estimation and Removal of Noise from a Single Image , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[15]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[16]  Nikolay N. Ponomarenko,et al.  A method for blind estimation of spatially correlated noise characteristics , 2010, Electronic Imaging.

[17]  Jean-Michel Morel,et al.  Self-similarity Driven Demosaicking , 2011, Image Process. Line.

[18]  Guillermo Sapiro,et al.  DCT image denoising: a simple and effective image denoising algorithm , 2011, Image Process. Line.

[19]  Jean-Michel Morel,et al.  Non-Local Means Denoising , 2011, Image Process. Line.

[20]  Marc Lebrun,et al.  An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm , 2012, Image Process. Line.

[21]  Pascal Getreuer,et al.  Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman , 2012, Image Process. Line.

[22]  Marc Lebrun,et al.  An Analysis and Implementation of the BM3D Image Denoising Method , 2012, Image Process. Line.

[23]  Jean-Michel Morel,et al.  A Nonlocal Bayesian Image Denoising Algorithm , 2013, SIAM J. Imaging Sci..

[24]  Jean-Michel Morel,et al.  Implementation of the "Non-Local Bayes" (NL-Bayes) Image Denoising Algorithm , 2013, Image Process. Line.

[25]  Boshra Rajaei.  An Analysis and Improvement of the BLS-GSM Denoising Method , 2014, Image Process. Line.

[26]  Jean-Michel Morel,et al.  A non-parametric approach for the estimation of intensity-frequency dependent noise , 2014, 2014 IEEE International Conference on Image Processing (ICIP).