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Poisson Image Restoration via Transformed Network

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Abstract

There is a Poisson inverse problem in biomedical imaging, fluorescence microscopy and so on. Since the observed measurements are damaged by a linear operator and further destroyed by Poisson noise, recovering the approximate original image is difficult. Motivated by the decouple scheme and the variance-stabilizing transformation (VST) strategy, we propose a method of transformed convolutional neural network (CNN) to restore the observed image. In the network, the Conv-layers play the role of a linear inverse filter and the distribution transformation simultaneously. Furthermore, there is no batch normalization (BN) layer in the residual block of the network, which is devoted to tackling with the non-Gaussian recovery procedure. The proposed method is compared with state-of-the-art Poisson deblurring algorithms, and the experimental results show the effectiveness of the method.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China

    Xiaoling Xu  (徐晓玲), Haiyu Zheng  (郑海玉), Fengqin Zhang  (张凤芹), Hechen Li  (李赫辰) & Minghui Zhang  (张明辉)

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  1. Xiaoling Xu  (徐晓玲)
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  2. Haiyu Zheng  (郑海玉)
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  3. Fengqin Zhang  (张凤芹)
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  4. Hechen Li  (李赫辰)
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  5. Minghui Zhang  (张明辉)
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Corresponding author

Correspondence to Minghui Zhang  (张明辉).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61661031)

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Xu, X., Zheng, H., Zhang, F. et al. Poisson Image Restoration via Transformed Network. J. Shanghai Jiaotong Univ. (Sci.) 26, 857–868 (2021). https://doi.org/10.1007/s12204-020-2235-7

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  • DOI: https://doi.org/10.1007/s12204-020-2235-7

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