工矿自动化2024，Vol.50Issue(2)：63-71,9.DOI:10.13272/j.issn.1671-251x.2023080098
基于密集残差连接U型网络的噪声图像超分辨率重建
Super resolution reconstruction of noisy images based on dense residual connected U-shaped networks
摘要
Abstract
The existing image super-resolution reconstruction networks are difficult to apply to noise intensive application scenarios in coal mines.Most networks improve performance by increasing depth,which leads to problems such as ineffective extraction of key features and loss of high-frequency information.In order to solve the above problems,a dense residual connected U-shaped network is proposed for super-resolution reconstruction of low resolution noisy images.The denoising module based on dense residual connections is introduced in the feature extraction path,fully extracting image features through dense connections.The features of residual learning are used to effectively denoise low resolution noisy images.The residual feature attention distillation module is introduced in the reconstruction path,by incorporating enhanced feature attention blocks into the residual blocks,different weights are assigned to features in different spaces to enhance the network's capability to extract key image features.The loss of image detail features is reduced in the residual blocks,thus better restoring image detail information.Comparative experiments are conducted on coal mine underground image datasets and public datasets,and the results show that in terms of objective evaluation index,structure similarity and image perception similarity of the proposed network are superior to the comparison network.It has a good balance in complexity and running speed.In terms of subjective visual effects,the image reconstructed by the proposed network basically eliminates the original image noise and effectively restores the detailed features of the image.关键词
噪声图像/超分辨率重建/密集残差连接/U型网络/去噪模块/残差特征注意力蒸馏模块Key words
noisy images/super resolution reconstruction/dense residual connections/U-shaped network/noise reduction module/residual feature attention distillation module分类
矿业与冶金引用本文复制引用
刘鹏南,李龙,张紫豪,朱星光,程德强..基于密集残差连接U型网络的噪声图像超分辨率重建[J].工矿自动化,2024,50(2):63-71,9.基金项目
国家重点研发计划项目(2021YFC2902702) （2021YFC2902702）
济宁市重点研发计划项目(2021JNZY013). （2021JNZY013）
