Image Denoising Using Dual Convolutional Neural Network with Skip Connection  

作　　者：Mengnan Lü Xianchun Zhou Zhiting Du Yuze Chen Binxin Tang 

机构地区：[1]School of Electronics Information Engineering,Nanjing University of Information Science&Technology,Nanjing 21004,China [2]School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing 210044,China

出　　处：《Instrumentation》2024年第3期74-85,共12页仪器仪表学报（英文版）

基　　金：funded by National Nature Science Foundation of China,grant number 61302188。

摘　　要：In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.

关 键 词：image denoising convolutional neural network skip connections multi-scale feature extraction network noise estimation network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]