基于残差密集卷积自编码的高噪声图像去噪方法  

Residual Dense Convolutional Autoencoder for High Noise Image Denoising

作　　者：张杰 卢淼鑫 李嘉康 徐大勇[2] 黄雯潇 史小平[3] ZHANG Jie;LU Miaoxin;LI Jiakang;XU Dayong;HUANG Wenxiao;SHI Xiaoping(College of Electrical and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;Key Lab of Tobacco Technology,Zhengzhou Tobacco Research Institute of CNTC,Zhengzhou 450000,China;Control and Simulation Center,Harbin Institute of Technology,Harbin 150080,China)

机构地区：[1]郑州轻工业大学电气信息工程学院,郑州450002 [2]中国烟草总公司郑州烟草研究院烟草工艺重点实验室,郑州450000 [3]哈尔滨工业大学控制与仿真中心,哈尔滨150080

出　　处：《计算机科学》2024年第S01期555-561,共7页Computer Science

基　　金：国家自然科学基金(62102373,62006213);河南省科技攻关项目(222102320321,232102220020)。

摘　　要：在高噪声图像去噪中,传统卷积自编码器难以挖掘有效的深度特征信息,进而影响了图像的重建质量。为了提高高噪声图像的重建质量,提出了一种残差密集卷积自编码器网络模型。该模型首先使用卷积操作代替池化操作以提高高噪声图像的表征能力;同时,在编码和解码阶段设计三级密集残差网络结构,实现图像特征的有效挖掘;最后,设计一个优化损失函数以进一步提高重建图像的质量。实验结果表明,设计的去噪方法能够从高噪声图像中重建高质量的图像,同时能够保留更多的细节特征信息,有效验证了该算法在图像去噪中的有效性。该方法能够有效解决高噪声图像的去噪问题,具有重要的应用价值。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.

关 键 词：图像去噪 卷积自编码器 残差密集卷积 高噪声图像 优化损失函数 

分 类 号：TN911.73[电子电信—通信与信息系统]