计算机应用研究2024，Vol.41Issue(1)：282-287,6.DOI:10.19734/j.issn.1001-3695.2023.07.0267
基于生成逆推的大气湍流退化图像复原方法
Restoration method for atmospheric turbulence degraded images based on generative inversion
摘要
Abstract
Atmospheric turbulence is a crucial factor that affects the quality of long-distance imaging.Though current deep learning models can effectively suppress geometric displacement and spatial blurring caused by atmospheric turbulence,such models require a large number of parameters and computational resources.To tackle this problem,this paper proposed a light-weight atmospheric turbulence degraded image restoration model based on generative inversion that entailed three core mo-dules:the DeBlur module,the remove shift module,and the turbulence regeneration module.The DeBlur module used high-dimensional feature mapping blocks,detail feature extraction blocks,and feature compensation blocks to suppress image blur-ring caused by turbulence.The remove shift module compensated for pixel displacement caused by turbulence using two convo-lutional layers.The turbulence regeneration module regenerated turbulence degraded images through convolutional operations.In the DeBlur module,it designed an attention-based feature compensation module that integrated the channel attention mecha-nism and the spatial mixed attention mechanism to focus on essential detail information in the image during training.The pro-posed model achieved peak signal-to-noise ratios of 19.94 dB and 23.51 dB,and structural similarity values of 0.688 2 and 0.752 1 on publicly available dataset Heat Chamber and self-built dataset Helen,respectively.Furthermore,it reduced the number of parameters and computational resources,compared to the current state-of-the-art(SOTA)method.The experimen-tal results demonstrate the effectiveness of this method in restoring atmospheric turbulence degraded images.关键词
大气湍流/退化图像复原/深度学习/注意力机制Key words
atmospheric turbulence/degraded image restoration/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
崔浩然,苗壮,王家宝,余沛毅,王培龙..基于生成逆推的大气湍流退化图像复原方法[J].计算机应用研究,2024,41(1):282-287,6.基金项目
江苏省自然科学基金资助项目 （）
