Infrared Image Deblurring Based on Dense Residual Generation Adversarial Network

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.