DnRFD:用于图像去噪的递进式残差融合密集网络  

DnRFD:Progressive Residual Fusion Dense Network for Image Denoising

作　　者：曹义亲[1] 饶哲初 朱志亮 张红斌[1] CAO Yiqin;RAO Zhechu;ZHU Zhiliang;ZHANG Hongbin(School of Software,East China Jiaotong University,Nanchang 330013,China;Institute of Software,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[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

出　　处：《计算机科学与探索》2022年第12期2841-2850,共10页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金(61861016);江西省科技支撑计划重点项目(20161BBE50081);江西省青年科学基金项目(20202BABL212006);江西省教育厅科学技术研究项目(GJJ190359)。

摘　　要：基于深度学习的去噪方法能够获得比传统方法更好的去噪效果,但是现有的深度学习去噪方法往往存在网络过深导致计算复杂度过大的问题。针对这个不足,提出一种用于去除高斯噪声的递进式残差融合密集网络(DnRFD)。该网络首先采用密集块来学习图像中的噪声分布,在充分提取图像局部特征的同时大幅降低网络参数;然后利用递进策略将浅层卷积特征依次与深层特征短线连接形成残差融合网络,提取出更多针对噪声的全局特征;最后将各密集块的输出特征图进行融合后输入给重建输出层,得到最后的输出结果。实验结果表明,在高斯白噪声等级为25和50时,该网络都能获得较高的峰值信噪比均值和结构相似性均值,并且去噪平均时间是DnCNN方法的一半,是FFDNet方法的1/3。总的来说,该网络整体去噪性能优于相关对比算法,可有效去除图像中的高斯白噪声和自然噪声,同时能更好地还原图像边缘与纹理细节。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]