Mixed Noise Removal by Bilateral Weighted Sparse Representation

Abstract

Image denoising is a fundamental but difficult task in image processing. Recovering a clean version from an image corrupted by mixed Gaussian and impulse noise is still challenging. The weighted sparse representation (WSR) method has been applied to deal with mixed noise and has achieved good performance. However, the WSR model presents an obvious disadvantage due to oversmoothing of the texture regions. In this paper, the novel bilateral weighted sparse representation model is presented for mixed noise removal. By introducing an image gradient-based weight, an adaptive sparse ratio is provided for each image patch to protect additional image details. In addition, a weighted method noise regularization term is proposed to utilize the global image information submerged in the method noise, which can improve the visual effect of recovered noise-free images. Both the subjective and objective performance are evaluated, and the experimental results show that the proposed method outperforms existing mixed noise removal algorithms, especially in terms of the visual performance.