The Noise Clinic: a Blind Image Denoising Algorithm

This paper describes the complete implementation of a blind image denoising algorithm, that takes any digital image as input. In a first step the algorithm estimates a Signal and Frequency Dependent (SFD) noise model. In a second step, the image is denoised by a multiscale adaptation of the Non-local Bayes denoising method. We focus here on a careful analysis of the denoising step and present a detailed discussion of the influence of its parameters. Extensive commented tests of the blind denoising algorithm are presented, on real JPEG images and on scans of old photographs. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article1.

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

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

[3]  Jean-Michel Morel,et al.  Multiscale Image Blind Denoising , 2015, IEEE Transactions on Image Processing.

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

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

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

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

[8]  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).

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

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

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

[12]  Marc Levoy,et al.  Gaussian KD-trees for fast high-dimensional filtering , 2009, ACM Trans. Graph..

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

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

[15]  Jean-Michel Morel,et al.  The noise clinic: A universal blind denoising algorithm , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[16]  Antoni Buades,et al.  Analysis and Extension of the Percentile Method, Estimating a Noise Curve from a Single Image , 2013, Image Process. Line.

[17]  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..

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

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

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

[21]  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.

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

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

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

[25]  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).

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

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