基于灰度补偿和特征融合的沙尘图像修复方法

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.