The noise clinic: A universal blind denoising algorithm

Most papers on denoising methods assume a white Gaussian noise model. Yet in most images handled by the public or by scientific users, the noise model is unknown and is not white, because of the various processes applied to the image before it reaches the user: scanning, demosaicing, compression, de-convolution, etc. To cope with this wide ranging problem, we propose a blind multiscale denoising algorithm working for noise which is simultaneously signal and frequency dependent. On noisy images coming from diverse sources (JPEG, scans of old photographs, ...) we show perceptually convincing results. This algorithm is compared to the state-of-the-art and it is also validated on images with white noise.

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

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

[3]  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.

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

[5]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[6]  Marc Lebrun.  An Analysis and Implementation of the BM3D Image , 2012 .

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

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

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

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

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

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

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

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

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

[16]  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..

[17]  Matthias Zwicker,et al.  Dual-domain image denoising , 2013, 2013 IEEE International Conference on Image Processing.

[18]  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).

[19]  Jean-Michel Morel,et al.  Secrets of image denoising cuisine* , 2012, Acta Numerica.

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

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

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

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

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