Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 38-42.doi: 10.11896/jsjkx.201000160

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Combining MCycleGAN and RFCNN to Realize High Resolution Reconstruction of Solar Speckle Image

CUI Wen-hao1, JIANG Mu-rong1, YANG Lei2, FU Peng-ming1, ZHU Ling-xiao1   

  1. 1 School of Information Science and Technology,Yunnan University,Kunming 650500,China
    2 Yunnan Observatories,Chinese Academy of Sciences,Kunming 650011,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:CUI Wen-hao,born in 1996,postgra-duate.His main research directions include deep learning and image reconstruction.
    JIANG Mu-rong,born in 1963,professor,Ph.D.Her main research directions include mathematical methods of image processing and intelligent calculation.
  • Supported by:
    Science and Technology Innovation Team Support Project of Yunnan Province (IRTSTYN) and National Natural Science Foundation of China (11773073).

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 reconstructing 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.Residual block and fusion layer are added to DeepFuse network to construct RFCNN,and multi frame reconstruction is carried out by using similar information between image frames.The edge of the reconstructed image is clearer.The reconstruction result is compared with the speckle mask method Level1+ used by Yunnan Observatory,which shows that the proposed algorithm has the advantages of small error and high definition of reconstructed image.

Key words: Deep learning, High resolution reconstruction, MCycleGAN, RFCNN, Solar speckle image

CLC Number: 

  • TP391.41
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