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Group Sparse Representation for Restoring Blurred Images with Cauchy Noise

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Abstract

For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose a new variational model based on the sparse representation of image groups, to recover blurred images with Cauchy noise. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed method outperforms other methods in terms of both visual perception and numerical indexes.

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Acknowledgements

Myungjoo Kang was supported by the National Research Foundation of Korea (2015R1A5A1009350, 2017R1A2A1A17069644).

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  1. Department of Mathematical Sciences, Seoul National University, Seoul, 151-747, Republic of Korea

    Sangwon Lee & Myungjoo Kang

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  1. Sangwon Lee
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Lee, S., Kang, M. Group Sparse Representation for Restoring Blurred Images with Cauchy Noise. J Sci Comput 83, 41 (2020). https://doi.org/10.1007/s10915-020-01227-8

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