Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230400073-7.doi: 10.11896/jsjkx.230400073

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Residual Dense Convolutional Autoencoder for High Noise Image Denoising

ZHANG Jie1, LU Miaoxin1, LI Jiakang2, XU Dayong2, HUANG Wenxiao1, SHI Xiaoping3   

  1. 1 College of Electrical and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China
    2 Key Lab of Tobacco Technology,Zhengzhou Tobacco Research Institute of CNTC,Zhengzhou 450000,China
    3 Control and Simulation Center,Harbin Institute of Technology,Harbin 150080,China
  • Published:2024-06-06
  • About author:ZHANG Jie,born in 1986,Ph.D,lectu-rer.His main research interests include image denoising and deep learning.
    XU Dayong,born in 1982,master,senior engineer.His main research interests include bacco processing technology and hyperspectral image processing.
  • Supported by:
    National Natural Science Foundation of China(62102373,62006213) and Henan Province Science and Technology Research Project(222102320321,232102220020).

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Abstract: In the field of high noise image denoising,traditional convolutional auto-encoders face challenges in extracting meaningful depth feature information,resulting in poor image reconstruction quality.To address this issue and improve the reconstruction quality of high noise images,this paper proposes a residual-density convolutional auto-encoder network model.The model firstly uses convolutional operations instead of pooling operations to improve the characterisation of high noise images.Moreover,a three-stage dense residual network structure is designed for effective image feature mining during the coding and decoding stages.Finally,an optimised loss function is designed to further improve the quality of the reconstructed images.Experimental results show that the denoising method presented in this paper is capable of reconstructing high quality images from high noise images while preserving more detailed feature information.It confirms the effectiveness of the algorithm in image denoising.The proposed method effectively addresses the challenge of denoising high noise images and has significant practical value.

Key words: Image denoising, Convolutional autoencoder, Residual dense convolution, High noise image, Optimized loss function

CLC Number: 

  • TN911.73
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