红外与毫米波学报2024，Vol.43Issue(2)：254-260,7.DOI:10.11972/j.issn.1001-9014.2024.02.015
基于梯度可感知通道注意力模块的红外小目标检测前去噪网络
Gradient-aware channel attention network for infrared small target image denoising before detection
摘要
Abstract
Infrared small target denoising is widely used in military and civilian fields.Existing deep learning-based methods are specially designed for optical images and tend to over-smooth the informative image details,thus losing the response of small targets.To both denoise and maintain informative image details,this paper pro-poses a gradient-aware channel attention network(GCAN)for infrared small target image denoising before detec-tion.Specifically,we use an encoder-decoder network to remove the additive noise of the infrared images.Then,a gradient-aware channel attention module is designed to adaptively enhance the informative high-gradient image channel.The informative target region with high-gradient can be maintained in this way.After that,we develop a large dataset with 3981 noisy infrared images.Experimental results show that our proposed GCAN can both effec-tively remove the additive noise and maintain the informative target region.Additional experiments of infrared small target detection further verify the effectiveness of our method.关键词
红外小目标/检测前去噪/梯度可感知通道注意力模块Key words
infrared small target/denoising before detection/gradient-aware channel attention分类
信息技术与安全科学引用本文复制引用
林再平,罗伊杭,李博扬,凌强,郑晴,杨晶贻,刘丽,吴京..基于梯度可感知通道注意力模块的红外小目标检测前去噪网络[J].红外与毫米波学报,2024,43(2):254-260,7.基金项目
Supported by the National Natural Science Foundation of China(62001478,61972435),Aviation Science Foundation Project Contract(ASFC-20165188004),Shanghai Aerospace Science and Technology Innovation Fund(SAST2021-035),Independent Research Fund of Key Laboratory of Military Scientific Research （62001478,61972435）
