Group-Based Sparse Representation for Image Restoration
Group-Based Sparse Representation for Image Restoration
复制标题
图像恢复的基于组的稀疏表示
DOI：
10.1109/tip.2014.2323127
复制
发表时间：
2014-08-01
影响因子：
10.6
通讯作者：
Gao, Wen
中科院分区：
文献类型：
--
作者：
Zhang, Jian;Zhao, Debin;Gao, Wen
传统的基于块的自然图像稀疏表示建模通常存在两个问题。首先，它必须解决字典学习中具有高计算复杂度的大规模优化问题。其次，在字典学习和稀疏编码中，每个补丁被独立地考虑，这忽略了补丁之间的关系，导致不准确的稀疏编码系数。在本文中，而不是使用补丁作为稀疏表示的基本单位，我们利用组的概念作为稀疏表示的基本单位，这是由具有相似结构的非局部补丁组成，并建立了一种新的稀疏表示模型，称为基于组的稀疏表示（GSR）。GSR能够在组域上稀疏地表示自然图像，在一个统一的框架内同时实现图像的内在局部稀疏性和非局部自相似性。此外，设计了一种有效的低复杂度的自适应字典学习方法，而不是从自然图像中学习字典。为了使GSR易于处理和强大的，分裂Bregman为基础的技术来解决建议的GSR驱动的最小化问题的图像恢复有效。大量的图像修复，图像去模糊和图像压缩感知恢复的实验表明，所提出的GSR建模优于许多当前最先进的方案在峰值信噪比和视觉感知。
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception.