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Blind image recovery approach combing sparse and low-rank regularizations

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Abstract

The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods.

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Acknowledgements

The authors would like to express their gratitude to the anonymous referees as well as the Editor and Associate Editor for their valuable comments, which led to substantial improvements of the paper. This work was supported by the National Natural Science Foundation of China (No. 61801199), the Natural Science Fund Project of Colleges in Jiangsu Province (No. 18KJB520017), the High-level Talent Scientific Research Foundation of Jinling Institute of Technology (No. jit-b-201801 and jit-b-201815) and the Major Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 19KJA510004).

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Authors and Affiliations

  1. School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Lei Feng, Jun Zhu & Lili Huang

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  1. Lei Feng
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  2. Jun Zhu
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  3. Lili Huang
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Correspondence to Lei Feng.

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Feng, L., Zhu, J. & Huang, L. Blind image recovery approach combing sparse and low-rank regularizations. Multimed Tools Appl 79, 18059–18070 (2020). https://doi.org/10.1007/s11042-019-08575-0

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  • DOI: https://doi.org/10.1007/s11042-019-08575-0

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