基于灰度补偿和特征融合的沙尘图像修复方法  

Sand-dust Image Restoration Using Gray Compensation and Feature Fusion

作　　者：丁伯圣 张睿恒 徐立新[1] 陈慧敏[1] DING Bosheng;ZHANG Ruiheng;XU Lixin;CHEN Huiming(Key Laboratory of Electromechanical Dynamic Control,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学机电动态控制重点实验室,北京100081

出　　处：《兵工学报》2023年第10期3115-3126,共12页Acta Armamentarii

摘　　要：沙尘颗粒对光的散射和吸收作用导致了沙尘天气下获取的可见光图像对比度低、颜色偏移严重,影响无人机、精确制导弹药对目标的识别与跟踪性能。由于场景结构复杂、参数估计困难等因素,现有的沙尘图像修复方法不能有效地提取图像中语义分量,导致修复后的图像颜色不真实、细节模糊。为此,提出基于灰度补偿的图像预处理模块和特征融合网络组成的沙尘图像修复框架。图像预处理模块对输入的沙尘图像进行灰度分布补偿来恢复场景中潜在的信息,并派生出颜色均衡和轮廓清晰的两个图像;融合网络对不同派生输入图像进行高维特征提取和融合,并恢复出高质量的图像。研究结果表明,所提出的方法修复图像取得了较高的指标统计值和良好的视觉效果,能有效提高沙尘图像的目标检测精度和分割准确率。Scattering and absorption of light by dust particles often result in sand-dust images with low contrast and significant color deviations,which can hinder the tracking performance of unmanned aerial vehicles and precision-guided ammunition for target recognition and tracking.Due to the complexity of scene structures,difficult parameter estimation and other factors,the existing sand-dust image restoration methods cannot effectively extract semantic components from images,resulting in unreal colors and blurred details of restored images.To address these issues,a two-stage sand dust image restoration framework consisting of gray compensation-based image pre-processing and feature fusion networks is proposed.The image pre-processing module compensates the gray distribution of the input sand images to recover latent scene information,producing two images with balanced color and clear contours.The fusion network then extracts and fuses high-dimensional features from different derived input images and restores high-quality images.The results show that high index statistics and good visual effects are obtained in the restored images,effectively improving detection and segmentation accuracy of sand-dust images.

关 键 词：灰度补偿 特征融合 派生输入 图像修复 卷积神经网络 目标识别 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]