UAV Remote Sensing Image Dehazing Based on Double-Scale Transmission Optimization Strategy

Abstract

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often have texture detail loss and color distortion problems, especially in highlighted regions. This is mainly due to the rich texture and low intensity of UAV remote sensing images being ignored, which results in incorrect transmission estimation. In this paper, we propose a UAV remote sensing image dehazing method based on double-scale transmission optimization strategy. First, we propose a double-scale optimization strategy to estimate the transmission map with more accurate texture details and color preservation, especially in highlighted regions of hazy UAV images that are most severely distorted. Second, a UAV-adaptive haze-line prior algorithm is proposed to address the large scene depth and low intensity of UAV remote sensing images. Finally, we introduce a luminance-weighted frequency domain saliency model to avoid texture detail loss and color distortions for better transmission optimization, especially in highlighted regions. Compared with state-of-the-art methods, our method shows better detail performance and visual effects, especially for UAV images with highlighted regions.