In the past decade, the sparsity prior of image is investigated and utilized widely as the development of compressed sensing theory. The dictionary learning combined with the convex optimization methods promotes the sparse representation to be one of the state-of-the-art techniques in image processing, such as denoising, super-resolution, deblurring, and inpainting. Empirically, the sparser of image representation, the better of image restoration. In this work, the non-local clustering sparse representation is applied with optimized matching strategies of self-similar patches, which break through the bottleneck of search window (localization) and improve the estimation effect of the sparse coefficient. The experimental results show that the proposed method provides an effective suppression on noise, preserves more details of image and presents more comfortable visual experience.

在过去的十年中，随着压缩感知理论的发展，图像的稀疏先验被广泛研究和利用。字典学习与凸优化方法相结合，将稀疏表示提升为图像处理中最先进的技术之一，例如去噪、超分辨率、去模糊和修复。根据经验，图像表示越稀疏，图像恢复效果越好。在这项工作中，非局部聚类稀疏表示与自相似补丁的优化匹配策略相结合，突破了搜索窗口（定位）的瓶颈，提高了稀疏系数的估计效果。实验结果表明，所提出的方法对噪声提供了有效的抑制，