Computer Science

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).

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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
[1] XIANG Y Y,LIU Z,JIN Z Y.High resolution solar image reconstruction method [J].Progress in astronomy,2016,1:94-110.
[2] HUO Z X,ZHOU J F.Method of astronomical image recon-struction from speckle pattern [J].Progress in Astronomy,2010(1):74-94.
[3] WU R G.Image super resolution reconstruction based on deep learning [D].Chengdu:University of Chinese Academy of Sciences (Institute of Optoelectronic Technology,Chinese Academy of Sciences),2020.
[4] GOODFELLOW I J,POUGET-ANADIE J.Genera-tive Adversarial Networks [J].Advances in Neural Information Processing Systems,2014(3):2672-2680.
[5] TANG C,LI J.Unpaired Low-Dose CT Denoising NetworkBased on Cycle-Consistent Generative Adversarial Network with Prior Image Information[J].Computational and Mathematical Methods in Medicine,2019(12):1-11.
[6] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//Computer Vision and Pattern Recognition.2014.
[7] LEDIG C,THEIS L.Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [C]//CVPR.2017:574-591.
[8] PRABHAKAR K R,SRIKAR V S,BABU R V.DeepFuse:A Deep Unsupervised Approach for Exposure Fusion with Ex-treme Exposure Image Pairs [C]//ICCV.2017:472.
[9] TAI Y,GUO Y,CHEN Q,et al.Auto-embedding generativeadversarialnetworks for high resolution image synthesis[J].IEEE Transactions on Multimedia,2019(6):112.
[10] ZENG R H ,XU H M,HUANG W B,et al.Dense regression networkfor video grounding[C]//IEEE Conference on Compu-ter Vision and Pattern Recognition.2020:618.
[11] LEDIG C,THEIS L,HUSZAR F,et al.Photo-Realistic SingleImage Super-Resolution Using a Generative Adversarial Network [C]//IEEE Conference on Computer Vision & Pattern Recognition(CVPR).2016:137.
[12] LEDIG C,THEIS L.Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [C]//CVPR.2017:574-591.
[13] ZHANG S L,YI B S,LI W Z,et al.Multi focus image fusion method based on image matting technology [J].Computer Applications,2016,36(7):1949-1953.
[14] LIU Y,LIU S,WANG Z.Multi-focus image fusion with dense SIFT[J].Information Fusion,2015,23:139-155.
[15] BEVILACQUA M,ROUMY A,GUILLEMOT C,et al.Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//The British Machine VisionConfe-rence(BMVC).2012.
[16] ZEYDE R,ELAD M,PROTTER M.On single image scale-up using sparse-representations[C]//Curves and Surfaces.Sprin-ger,2012:711-730.
[17] MARTIN D,FOWLKES C,TAL D,et al.A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]//IEEE International Conference on Computer Vision (ICCV).2001:416-423.
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