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.

图像去噪是图像处理中的基本但困难的任务。从混合高斯和脉冲噪声破坏的图像中恢复干净的版本仍然很困难。加权稀疏表示（WSR）方法已被应用到混合噪声处理中，并取得了良好的性能。然而，由于纹理区域的过度平滑，WSR模型存在明显的缺点。本文提出了一种新颖的双边加权稀疏表示模型，用于混合噪声去除。通过引入基于图像梯度的权重，为每个图像块提供了自适应稀疏比，以保护其他图像细节。另外，提出了加权方法噪声正则化项，以利用淹没在方法噪声中的全局图像信息，可以改善恢复的无噪点图像的视觉效果。评估了主观和客观性能，实验结果表明，该方法优于现有的混合噪声消除算法，尤其是在视觉性能方面。