Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for Image Denoising

被引：6
|
作者：
Zhang, Jing [1 ]
Sang, Liu [1 ]
Wan, Zekang [1 ]
Wang, Yuchen [1 ]
Li, Yunsong [1 ]
机构：
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Peoples R China
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP) | 2020年
关键词：
image denoising; multi-scale; convolutional network; diamond;
D O I：
10.1109/vcip49819.2020.9301843
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.
引用
收藏
页码：213 / 216
页数：4
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