Li Jinyang, Liu Zhijing, Yao Yong
School of Computer Science and Technology, Xidian University, Xi'an 710071, Shannxi, China.
Sensors (Basel). 2018 Apr 8;18(4):1135. doi: 10.3390/s18041135.
Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients' distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively.
稀疏表示已被证明是一种在各种图像恢复应用中非常有效的技术。本文提出了一种基于改进稀疏表示的方法来检测和估计成像传感器的散焦模糊。考虑到模式通常在不同图像或单个图像的不同块之间有显著变化，在过完备字典上进行稀疏表示是不稳定且耗时的。我们提出了一种自适应域选择方案，以预学习一组紧凑字典，并为每个图像块自适应地选择最优字典。然后，利用非局部结构相似性，该方法学习更接近真实分布的非零均值系数分布。可以获得更准确的稀疏系数，并进一步提高结果的性能。实验结果验证了该方法在定性和定量方面均优于现有的散焦模糊估计方法。
