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Non-blind deconvolution with ℓ 1 -norm of high-frequency fidelity

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Abstract

Non-blind deconvolution has been a long-standing challenge of both image structures preservation and blur and noise removal. However, most existing methods conduct the direct deconvolution on the degraded image, and overlook the difference between low-frequency and high-frequency of the image. Based on the observation that high-frequency (e.g., edges and structures) is more important than low-frequency in image deblurring, we present a novel method for non-blind deconvolution by incorporating the ℓ 1 -norm fidelity of image high-frequency. Firstly, the ℓ 1 -norm fidelity of image high-frequency is proposed in the overall objective function for image structures preservation and noise suppression, and then alternating minimization iterative method is employed to estimate high-frequency components of the image. Secondly, high-frequency estimations are taken as constraint terms, and least square integration and fast fourier transform are efficiently exploited to recover the ideal image. Finally, experimental simulations demonstrate that the proposed algorithm outperforms other state-of-the-art methods in both subjective and objective assessments.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61571382, 61571005, 81301278, 61172179, and 61103121, in part by the Guangdong Natural Science Foundation under Grant 2015A030313007, in part by the Fundamental Research Funds for the Central Universities under Grant 20720160075, 20720150169 and 20720150093, and in part by the Research Fund for the Doctoral Program of Higher Education under Grant 20120121120043.

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Authors and Affiliations

  1. Xiamen University, Xiamen, China

    Peixian Zhuang, Yue Huang, Delu Zeng & Xinghao Ding

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  1. Peixian Zhuang
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  2. Yue Huang
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  3. Delu Zeng
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  4. Xinghao Ding
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Correspondence to Xinghao Ding.

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Zhuang, P., Huang, Y., Zeng, D. et al. Non-blind deconvolution with ℓ 1 -norm of high-frequency fidelity. Multimed Tools Appl 76, 23607–23625 (2017). https://doi.org/10.1007/s11042-016-4083-x

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  • DOI: https://doi.org/10.1007/s11042-016-4083-x

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