Image De-Noising Based on Block Diagonal Representation

Abstract

IMAGE de-noising plays an important role in modern image processing systems. Image Filtering is challenging in terms of both efficiency and effectiveness. Patch similarity is major concern in filtering. By grouping similar patches to utilize the self-similarity and sparse linear approximation of natural images, recent nonlocal and transform domain methods have been widely used in colour image de-noising. The importance of the patch level representation is understated. In this paper, we mainly investigate the influence and potential of representation at patch level by considering a general formulation with block diagonal matrix. We further show that by training a proper global patch basis, along with a local principal component analysis transform in the grouping dimension, a simple transform-threshold-inverse method could produce very competitive results.