Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

TL;DR

This paper introduces a variational algorithm that effectively removes mixed Gaussian and impulse noise from images by leveraging both local smoothness and nonlocal self-similarity, outperforming existing methods.

Contribution

The paper proposes a novel variational framework combining local and nonlocal image consistency for mixed noise removal, solved efficiently with a Split-Bregman technique.

Findings

Significant performance improvements over state-of-the-art methods.

Effective handling of mixed Gaussian and impulse noise.

Validation through extensive experiments.

Abstract

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.