LUO J W, ZHANG Z, LUO F,et al.Multi-scale attention interactive network for image denoising[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(5):144-153.

doi:10.16186/j.cnki.1673-9787.2021110115

Received:2021/11/26

Revised:2022/01/16

Published:2023/09/25

Multi-scale attention interactive network for image denoising

LUO Junwei, ZHANG Zhen, LUO Fen, QIAO Yingxu, HUO Zhanqiang

School of Computer Science and Technology，Henan Polytechnic University，Jiaozuo 454000，Henan，China

Abstract:In the image denoising，aiming at the problems of incomplete detail information and low feature utilization of the image extracted by denoising network，a multi-scale attention interactive image denoising network（MAINet）based on deep learning was proposed.Firstly，for shallow pixel-level features，the multi-scale feature extraction block was used to obtain rich context information and image texture features to ensure the integrity of image information.Secondly，a dual-branch attention mechanism was introduced to guide the network to obtain more discriminative feature information，suppress undesired noise，and further optimize the feature information.Finally，the dense connection and the paired convolution operation of the classified dense residual blocks were used to enhance the interaction of the model，and the global multi-level features were jointly learned to extract higher-quality semantic-level features to improve the performance of the denoising network.Experiment results showed that the proposed denoising network had improved the denoising effects on both synthetic noise and real noise datasets in terms of quantitative and qualitative evaluation.

Key words:deep learning;image denoising;multi-scale feature extraction;dual-branch attention mechanism;classified dense residual block

多尺度注意力交互式图像去噪网络_罗军伟.pdf