PROJECT TITLE :

NLH A Blind Pixel-Level Non-Local Method for Real World Image Denoising

ABSTRACT:

Using non-local self similarity (NSS) as an image denoising prior is powerful. Patch-level NSS priors are used in most existing denoising algorithms. A pixel-level NSS prior is introduced in this research, which means searching for similar pixels in a non-local area. This is due to the fact that it is easier to discover roughly similar pixels in artificial images than it is to find similar patches in natural photos. NSS priors allow us to construct an accurate noise level estimate approach and then a blind picture denoising method using the lifting Haar transform and Wiener filtering techniques, respectively. Proposed methods perform better on benchmark datasets than earlier non-deep methods and are still competitive with current state of the art deep learning based methods for denoising images from the real world.