Perception-oriented UAV Image Dehazing Based on Super-Pixel Scene Prior

Current UAV image defogging techniques often result in images with chromatic aberrations, color distortions, and increased noise due to flight variables and environmental factors, impacting downstream mission objectives. This paper presents a novel UAV image dehazing framework designed to enhance perceptual tasks in foggy conditions. Moving beyond traditional pixel-level dehazing, our approach utilizes a Super-Pixel Scene Prior (SPSP) method, improving the UAV defogging process. By shifting the dehazing operation from RGB to Lab color space using SPSP, we minimize chromatic confusion and pinpoint reliable defogging areas, especially in the L channel. To address light inconsistency challenges during defogging, we introduce a new guided filtering algorithm that leverages Simple Linear Iterative Clustering (SLIC). This algorithm utilizes super-pixel clusters instead of large guided windows, preserving crucial information and boosting efficiency. SPSP also guides the SLIC algorithm in Lab space, facilitating faster defogging. Our framework incorporates a quantitative analysis of super-pixel segmentation and target detection, utilizing a feedback loop with the Alternating Direction Multiplier Method (ADMM) to optimize perception and defogging concurrently, thus enhancing UAV visual capabilities in fog. Our UAV image dehazing technique outperforms existing methods, as evidenced by quantitative and qualitative assessments, effectively eliminating haze from UAV images and significantly improving perceptual processing in foggy conditions.