School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2019 Mar 6;19(5):1143. doi: 10.3390/s19051143.
Sparse representation is a powerful statistical technique that has been widely utilized in image restoration applications. In this paper, an improved sparse representation model regularized by a low-rank constraint is proposed for single image deblurring. The key motivation for the proposed model lies in the observation that natural images are full of self-repetitive structures and they can be represented by similar patterns. However, as input images contain noise, blur, and other visual artifacts, extracting nonlocal similarities only with patch clustering algorithms is insufficient. In this paper, we first propose an ensemble dictionary learning method to represent different similar patterns. Then, low-rank embedded regularization is directly imposed on inputs to regularize the desired solution space which favors natural and sharp structures. The proposed method can be optimized by alternatively solving nuclear norm minimization and l 1 norm minimization problems to achieve higher restoration quality. Experimental comparisons validate the superior results of the proposed method compared with other deblurring algorithms in terms of visual quality and quantitative metrics.
稀疏表示是一种强大的统计技术，已广泛应用于图像恢复应用中。在本文中，我们提出了一种基于低秩约束的改进稀疏表示模型，用于单图像去模糊。所提出模型的主要动机在于观察到自然图像充满了自我重复的结构，并且它们可以由相似的模式来表示。然而，由于输入图像包含噪声、模糊和其他视觉伪影，仅使用补丁聚类算法提取非局部相似性是不够的。在本文中，我们首先提出了一种集成字典学习方法来表示不同的相似模式。然后，直接对输入施加低秩嵌入正则化，以正则化有利于自然和清晰结构的所需解空间。可以通过交替求解核范数最小化和 l1 范数最小化问题来优化所提出的方法，以实现更高的恢复质量。实验比较验证了与其他去模糊算法相比，该方法在视觉质量和定量指标方面具有更好的结果。
