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Tai, Y., Yang, J. and Liu, X. (2017) Image Super-Resolution via Deep Recursive Residual Network. IEEE Conference on Computer Vision & Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 112.
https://doi.org/10.1109/CVPR.2017.298

has been cited by the following article:

  • TITLE: Using MCycleGAN to Realize High-Resolution Reconstruction of Solar Speckle Image

    AUTHORS: Wenhao Cui, Murong Jiang, Pengming Fu, Lingxiao Zhu, Lei Yang

    KEYWORDS: Solar Speckle Image, High-Resolution Reconstruction, MCycleGAN

    JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.11, November 26, 2020

    ABSTRACT: High-resolution reconstruction of solar speckle image is one of the important research contents in astronomical image processing. High-resolution image reconstruction based on deep learning can obtain the end-to-end mapping function from low-resolution image to high-resolution image through neural network model learning, which can recover the high-frequency information of the image. However, when used to reconstruct the sun speckle image with single feature, more noise and fuzzy local details, there are some shortcomings such as too smooth edge and easy loss of high-frequency information. In this paper, the structure features of input image and reconstructed image are added to CycleGAN network to get MCycleGAN. High frequency information is obtained from structural features by generator network, and the feature difference is calculated to enhance the ability of network to reconstruct high-frequency information. The edge of the reconstructed image is clearer. Compared with the speckle mask method level 1+ used by Yunnan Observatory, the results show that the proposed algorithm has the advantages of small error, fast reconstruction speed and high image clarity.