UAV Image Haze Removal Based on Saliency- Guided Parallel Learning Mechanism

Abstract

Current haze removal methods for unmanned aerial vehicle (UAV) images are mostly based on natural image dehazing methods, which ignore the particular imaging mechanism of UAVs. They often fail to restore regions with rich spectral and textural information. In this letter, we propose a saliency-guided parallel learning mechanism for UAV image haze removal. First, we design a saliency-guided parallel dehazing module with two parallel paths. The residual feature extraction path obtains deep-level features to realize global dehazing effectively. The key feature enhancement path, which comprises saliency dense blocks, realizes local textural preservation and spectral restoration. Second, a sporadic foreground saliency detection method is proposed for UAV images with sporadic objects. The saliency map guides the learning of significant spectral and textural information in hazy images. Finally, a multiscale reconstruction module is introduced to more accurately estimate small-scale textural details and large-scale spectral information. Experimental results show that the proposed method has better detail performance and visual effects than state-of-the-art methods.