红外技术2024，Vol.46Issue(6)：663-671,9.
基于密集残差生成对抗网络的红外图像去模糊
Infrared Image Deblurring Based on Dense Residual Generation Adversarial Network
摘要
Abstract
During infrared(IR)image capture,the shaking of camera equipment or rapid movement of the target causes motion blur in the image,significantly affecting the extraction and recognition of effective information.To address these problems,this study proposes an infrared image deblurring method based on a dense residual generation adversarial network(DeblurGAN).First,multiscale convolution kernels are employed to extract features at different scales and levels from infrared images.Second,a residual-in-residual dense block(RRDB)is used,instead of the residual unit in the original generation network,to improve the detail of the recovered IR images.Experiments were conducted on the infrared image dataset collected by our group,and the results show that compared to DeblurGAN,the proposed method improves PSNR by 3.60 dB and SSIM by 0.09.The subjective deblurring effect is better,and the recovered infrared images have clear edge contours and detail information.关键词
生成对抗网络/密集残差块/红外图像/去运动模糊Key words
generative adversarial network/residual-in-residual dense block/infrared image/motion deblurring分类
计算机与自动化引用本文复制引用
李立,易诗,刘茜,程兴豪,王铖..基于密集残差生成对抗网络的红外图像去模糊[J].红外技术,2024,46(6):663-671,9.基金项目
四川省自然科学基金面上项目(24NSFSC1481),成都理工大学高等教育人才培养质量和教学改革项目(JG2130216). （24NSFSC1481）
