Institute of Computer Science and Technology, Peking University, Beijing 100871, China.
IEEE Trans Image Process. 2013 Apr;22(4):1456-69. doi: 10.1109/TIP.2012.2231690. Epub 2012 Dec 4.
Image prior models based on sparse and redundant representations are attracting more and more attention in the field of image restoration. The conventional sparsity-based methods enforce sparsity prior on small image patches independently. Unfortunately, these works neglected the contextual information between sparse representations of neighboring image patches. It limits the modeling capability of sparsity-based image prior, especially when the major structural information of the source image is lost in the following serious degradation process. In this paper, we utilize the contextual information of local patches (denoted as context-aware sparsity prior) to enhance the performance of sparsity-based restoration method. In addition, a unified framework based on the markov random fields model is proposed to tune the local prior into a global one to deal with arbitrary size images. An iterative numerical solution is presented to solve the joint problem of model parameters estimation and sparse recovery. Finally, the experimental results on image denoising and super-resolution demonstrate the effectiveness and robustness of the proposed context-aware method.
基于稀疏和冗余表示的图像先验模型在图像恢复领域越来越受到关注。传统的基于稀疏性的方法独立地对小图像块施加稀疏先验。然而，这些方法忽略了相邻图像块稀疏表示之间的上下文信息。这限制了基于稀疏性的图像先验的建模能力，特别是当源图像的主要结构信息在后续严重退化过程中丢失时。在本文中，我们利用局部补丁的上下文信息（表示为上下文感知稀疏先验）来增强基于稀疏性的恢复方法的性能。此外，还提出了一个基于马尔可夫随机场模型的统一框架，将局部先验调整为全局先验，以处理任意大小的图像。提出了一种迭代数值解法来解决模型参数估计和稀疏恢复的联合问题。最后，在图像去噪和超分辨率实验结果上验证了所提出的上下文感知方法的有效性和鲁棒性。
