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Single Image Blind Deblurring Based on Salient Edge-Structures and Elastic-Net Regularization

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Abstract

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.

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Acknowledgements

This work was supported by Science and Technology Planning Project of Guangdong Province under the grant 2018B010108001 and 2017B030306017, and YangFan Innovative and Entrepreneurial Research Team Project of Guangdong Province under the grant 2016YT03G125.

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  1. College of Automation Science and Technology, South China University of Technology, Guangzhou, 510640, China

    XiaoYuan Yu & Wei Xie

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  1. XiaoYuan Yu
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Correspondence to Wei Xie.

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Yu, X., Xie, W. Single Image Blind Deblurring Based on Salient Edge-Structures and Elastic-Net Regularization. J Math Imaging Vis 62, 1049–1061 (2020). https://doi.org/10.1007/s10851-020-00949-6

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