PROJECT TITLE :

Multi scale Image Blind De noising - 2015

ABSTRACT:

Arguably many thousands papers are dedicated to image denoising. Most papers assume a fastened noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw pictures. Nevertheless, in most pictures handled by the general 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 typically by chemical suggests that). For such pictures, recent progress in noise estimation permits to estimate from a single image a noise model, that is simultaneously signal and frequency dependent. We propose here a multiscale denoising algorithm tailored to this broad noise model. This ends up in a blind denoising algorithm which we tend to demonstrate on real JPEG images and on scans of old pictures 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 distinctive state of the art previous blind denoising method.