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Natural image deblurring based on L0-regularization and kernel shape optimization

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Abstract

The goal of blind image deblurring is to estimate the blur kernel and restore the sharp latent image based on an input blur image. This paper proposes a novel blind image deblurring algorithm based on L0-regularization and kernel shape optimization. Firstly, the proposed objective function of the optimization model is formulated with L0-norm terms of the gradient and intensity of kernels, which results to good sparsity and less noise in the obtained kernel. Then, the coarse-to-fine iterative framework is adopted to estimate reliable salient image structures implicitly, which can reduce computation and accelerate convergence. Finally, the kernel shape is optimized by weighting method, which enables the obtained kernel closer to the ground-truth. Experimental results on public bench mark datasets demonstrate that restored images are clear with less ring-artifacts.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Authors and Affiliations

  1. School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Fengjun Zhang, Wei Lu, Hongmei Liu & Fei Xue

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wei Lu

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  1. Fengjun Zhang
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  2. Wei Lu
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  3. Hongmei Liu
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  4. Fei Xue
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Correspondence to Fei Xue.

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Zhang, F., Lu, W., Liu, H. et al. Natural image deblurring based on L0-regularization and kernel shape optimization. Multimed Tools Appl 77, 26239–26257 (2018). https://doi.org/10.1007/s11042-018-5847-2

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  • DOI: https://doi.org/10.1007/s11042-018-5847-2

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