Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2841-2850.DOI: 10.3778/j.issn.1673-9418.2103030

• Graphics and Image • Previous Articles     Next Articles

DnRFD:Progressive Residual Fusion Dense Network for Image Denoising

CAO Yiqin1, RAO Zhechu1, ZHU Zhiliang1,2,+(), ZHANG Hongbin1   

  1. 1. School of Software, East China Jiaotong University, Nanchang 330013, China
    2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2021-03-10 Revised:2021-04-27 Online:2022-12-01 Published:2021-04-30
  • About author:CAO Yiqin, born in 1964, M.S., professor, member of CCF. His research interests include image processing and pattern recognition.
    RAO Zhechu, born in 1997, M.S. candidate. His research interest is image processing.
    ZHU Zhiliang, born in 1988, Ph.D., lecturer, member of CCF. His research interests include image information processing, virtual reality and human-computer interaction.
    ZHANG Hongbin, born in 1979, Ph.D., associate professor, member of CCF. His research interests include computer vision, natural language processing and recommendation systems.
  • Supported by:
    National Natural Science Foundation of China(61861016);Key Project of Science and Technology Support Plan of Jiangxi Province(20161BBE50081);Youth Science Foundation of Jiangxi Province(20202BABL212006);Science and Technology Research Project of Education Department of Jiangxi Province(GJJ190359)


曹义亲1, 饶哲初1, 朱志亮1,2,+(), 张红斌1   

  1. 1.华东交通大学 软件学院,南昌 330013
    2.中国科学院 软件研究所,北京 100190
  • 通讯作者: +E-mail:
  • 作者简介:曹义亲(1964—),男,江西九江人,硕士,教授,CCF会员,主要研究方向为图像处理、模式识别。
  • 基金资助:


The denoising method based on deep learning can achieve better denoising effect than the traditional method, but the existing deep learning denoising methods often have the problem of excessive computational complexity caused by too deep network. To solve this problem, a progressive residual fusion dense network (DnRFD) is proposed to remove Gaussian noise. Firstly, dense blocks are used to learn the noise distribution in the image, and the network parameters are greatly reduced while the local features of the image are fully extracted. Then, a progressive strategy is used to connect the shallow convolution features with the deep features to form a residual fusion network to extract more global features for noise. Finally, the output characteristic images of each dense block are fused and input to the reconstructed output layer to get the final output result. Experimental results show that, when the Gaussian white noise level is 25 and 50, the network can achieve higher mean PSNR and mean structural similarity, and the average time of denoising is half of the DnCNN method and one third of the FFDNet method. In general, the overall denoising performance of the network is better than that of the correlative comparison algorithms, and it can effectively remove the white Gaussian noise and natural noise in the image, and can restore the edge and texture details of the image better.

Key words: image denoising, deep learning, dense block, residual learning, progressive residual fusion



关键词: 图像去噪, 深度学习, 密集块, 残差学习, 递进式残差融合

CLC Number: