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Single Image Blind Deblurring Based on Salient Edge-Structures and Elastic-Net Regularization
Journal of Mathematical Imaging and Vision ( IF 2 ) Pub Date : 2020-02-19 , DOI: 10.1007/s10851-020-00949-6
XiaoYuan Yu , Wei Xie

In single image blind deblurring, the blur kernel and latent image are estimated from a single observed blurry image. The associated mathematical problem is ill-posed, and an acceptable solution is difficult to obtain without additional priors or heuristics. Inspired by the nonlocal self-similarity in image denoising problem, we introduce elastic-net regularization as a rank prior to improve the estimation of the intermediate image. Furthermore, it is well known that salient edge-structures can provide reliable information for kernel estimation. Therefore, we propose a new blind image deblurring method by combining the salient edge-structures and the elastic-net regularization. The salient edge-structures are selected from the intermediate image and used to guide the estimation of the blur kernel. Then, we employ the elastic-net regularization and edge-structures to further estimate intermediate latent image, by retaining the dominant edge and removing slight texture, for a better kernel estimation. Finally, quantitative and qualitative evaluations are conducted by comparing the results with those obtained by state-of-the-art methods. We conclude that the proposed method performs favorably when considering both synthetic and real blurry images.

中文翻译:

基于显着边缘结构和弹性网正则化的单图像盲去模糊

在单图像盲去模糊中,模糊内核和潜像是从单个观察到的模糊图像中估计的。相关的数学问题是不适定的,并且如果没有其他先验或启发式方法就很难获得可接受的解决方案。受图像去噪问题中非局部自相似性的启发,我们引入了弹性网正则化作为等级,以改善中间图像的估计。此外,众所周知,显着的边缘结构可以为核估计提供可靠的信息。因此,我们提出了一种结合显着边缘结构和弹性网正则化的盲图像去模糊新方法。从中间图像中选择显着的边缘结构,并将其用于指导模糊核的估计。然后,我们使用弹性网正则化和边缘结构通过保留优势边缘并去除轻微纹理来进一步估计中间潜像,以获得更好的核估计。最后,通过将结果与通过最新方法获得的结果进行比较,进行定量和定性评估。我们得出的结论是,在同时考虑合成图像和真实模糊图像时,所提出的方法性能良好。
更新日期:2020-02-19
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