摘要
针对雨天环境下获取图像质量差,导致后续机器视觉任务效率低下的问题,提出一种基于多特征交互和密集残差的图像去雨算法.首先,提出多重特征交互卷积模块提取不同空间下雨线的语义特征,增强信息利用程度;其次,构建多维空间权重注意模块,将不同空间信息权重初步融合并增强雨线特征;然后,结合密集连接和残差网络的优点,设计一种密集残差融合模块,在提高网络学习能力的同时实现对信息的重复利用,进一步校正雨纹信息;最后,通过将多种损失函数线性组合,并结合雨天成像模型提高输出图像质量.在多个公开数据集上的实验结果表明,本文所提算法的主客观评价指标均优于所对比的经典及新颖算法,在去除雨纹的同时能更有效地保留图像背景细节信息.
To solve the poor image quality and subsequent low efficiency of machine vision tasks on rainy days,an image rain removal algorithm based on multi-feature interaction and dense residual is proposed.First,a multi-feature interactive convolution module is proposed to extract the semantic features of rain streaks in different spaces to enhance information utilization.Second,a multidimensional space weight attention module is constructed,and the weights of different spatial information are preliminarily integrated to enhance the characteristics of rain streaks.Then combining the advantages of dense connection and residual network,a dense residual fusion module is designed,which improves the learning ability of the network,realizes the reuse of information,and further corrects the rain information.Finally,the output image quality is improved through the linear combination of various loss functions as well as the rainy day imaging model.Experiments on several public datasets show that the subjective and objective evaluation indexes of the proposed algorithm outperform those of the classical algorithm and novel algorithms,and the detailed background information of the images can be better preserved while removing the rain streaks.
作者
林森
邱庆澳
LIN Sen;QIU Qingao(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China)
出处
《南京信息工程大学学报》
CAS
北大核心
2024年第4期472-481,共10页
Journal of Nanjing University of Information Science & Technology
基金
国家重点研发计划(2018YFB1403303)
辽宁省教育厅高等学校基本科研项目(LJKMZ20220615)。
关键词
图像去雨
机器视觉
密集残差
深度学习
image rain removal
machine vision
dense residual
deep learning
