Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 101-106.doi: 10.11896/jsjkx.200600144

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Medical Image Deblur Using Generative Adversarial Networks with Channel Attention

WANG Jian-ming1, LI Xiang-feng1, YE Lei1, ZUO Dun-wen1, ZHANG Li-ping2   

  1. 1 College of Mechanical and Electrical Engineering,Nanjing University of Aeronautic and Astronautics,Nanjing 210016,China
    2 College of Science,Nanjing University of Aeronautic and Astronautics,Nanjing 210016,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:WANG Jian-ming,born in 1995,postgraduate.His main research intrests include deep learning and image proces-sing.
    LI Xiang-feng,born in 1971,professor,Ph.D supervisor.Her main research intrests include computer intelligent processing and antifatigue manufacture.
  • Supported by:
    Joint Funds of the National Natural Science Foundation of China(U20A20293).

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Abstract: Clear medical images can effectively help doctors to make pathological analysis and diagnosis.Aiming at the problem of image blur caused by camera unfocused during the process of medical image acquisition,this paper proposes a new image deblurring network based on the deblur generative adversarial networks(DeblurGAN).The network uses channel attention structure in Generator and extracts details effectively.During the process of image up-sampling,we use the method of bilinear interpolation with a convolution layer instead of transpose convolution,which removes the checkerboard effects.The model is trained by the combination of adversarial loss and content loss to obtain clear image.The experimental results show that the network achieves better performance in both PSNR and SSIM compared with DeblurGAN.

Key words: Channel attention structure, Convolutional neural network, Generative adversarial networks, Image deblur, Medical images, Residual network

CLC Number: 

  • TP391
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