Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
This addresses a practical issue in image processing for applications like acquisition and transmission, but it is incremental as it builds on existing variational and consistency-based approaches.
The paper tackles the problem of removing mixed Gaussian-impulse noise from images, which is common in practice but not handled by most single-noise denoising algorithms, and achieves significant performance improvements over state-of-the-art methods.
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.