基于非局部稀疏编码的超分辨率图像复原

基于压缩感知的超分辨率图像复原方法通常采用局部稀疏编码策略，对每一图像块独立编码，易产生人工的分块效应。针对上述问题，该文提出一种基于非局部稀疏编码的超分辨率图像复原方法。该算法在字典训练和图像编码过程中分别运用图像的非局部自相似先验知识，即利用低分辨率图像的插值图像训练字典，并通过计算相似块局部编码的加权平均，得到每一图像块的非局部稀疏编码。仿真实验表明，所提算法能够获得更优的复原效果，并且对于含噪图像具有较强的鲁棒性。

Super-resolution Image Restoration Based on Nonlocal Sparse Coding

Super-resolution image restoration methods based on Compressive Sensing (CS) generally adopt local sparse coding strategy. Such strategy encodes each image block independently, which easily induces artificial blocking effect. To overcome this problem, a super-resolution image restoration method based on nonlocal sparse coding is proposed. The nonlocal self-similarity of image is considered as a prior in the dictionary training and image coding processes, respectively. Specifically, the proposed algorithm trains the dictionary with interpolated low-resolution images, and calculates the weighted average local code of similar patches, in order to obtain the nonlocal sparse code of each image block. Numerical experiments suggest that the proposed algorithm has a good recovery performance, and is robust to image noise.