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Super-Resolution Reconstruction via Multi-frame Defocused Images Based on PSF Estimation and Compressive Sensing

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Abstract

Image super-resolution reconstruction is an effective method to improve image resolution, but most reconstruction methods rely on the clear low resolution images ignoring the blurred images which are also effective observations of the scene. Aiming at the problem, a super-resolution reconstruction (SRR) method via multi-frame defocused images is proposed. Firstly, according to the image degraded model, we establish the cost function of the point spread function (PSF) and utilize the particle swarm optimization algorithm to estimate it. Then, based on the multi-frame defocused images and PSFs, a joint reconstruction model is established to realize SRR by compressive sensing (CS) theory. In the CS framework, only the interpolated version of the low-resolution image is used for training purpose and the K-Singular Value Decomposition method is used for dictionary training. In addition, to solve the edge effect problem, an internal blur matrix is constructed according to the image blurring process, and a weight coefficient is introduced in the patch splicing process. Experiments show that the proposed algorithm can accurately estimate the defocused image PSF and achieve a good reconstruction effect.

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Acknowledgements

This work is supported by the project of National Natural Science Foundation of China (61571068) and the innovative research projects of colleges and universities in Chongqing (12A19369).

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Authors and Affiliations

  1. State Key Lab of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, 400044, China

    Yuxing Mao, Haiwei Jia, Chao Li & Yan Yan

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  1. Yuxing Mao
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  2. Haiwei Jia
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  3. Chao Li
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  4. Yan Yan
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Correspondence to Haiwei Jia.

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Mao, Y., Jia, H., Li, C. et al. Super-Resolution Reconstruction via Multi-frame Defocused Images Based on PSF Estimation and Compressive Sensing. Sens Imaging 19, 25 (2018). https://doi.org/10.1007/s11220-018-0210-2

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  • DOI: https://doi.org/10.1007/s11220-018-0210-2

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