Exploring Dehazing Methods For Remote Sensing Imagery: A Review

Remote sensing imagery plays a pivotal role in numerous applications, from environmental monitoring to disaster management. However, the occurrence of haze which is atmospheric often reduces the quality and interpretability of these images.  Atmospheric Haze reduces visibility of remote sensed images by reducing contrast and causing colour distortions.  Dehazing techniques are employed to improve the perceptibility and clarity affected images by haze. In this review, we delve into the realm of dehazing methods specifically tailored for remote sensing imagery, aiming to shed light on their efficacy and applicability. We focus on a comprehensive comparison of four prominent dehazing techniques: Histogram Equalization (HE), Light Channel Prior (LCP), Contrast Enhancement Filters (CEF), and Dark Channel Prior (DCP). These methods, representing a spectrum of approaches, are evaluated based on key quality metrics of images, including PSNR, MSE and SSIM

Physical Perception Network and an All-weather Multi-modality Benchmark for Adverse Weather Image Fusion

Multi-modality image fusion (MMIF) integrates the complementary information from different modal images to provide comprehensive and objective interpretation of a scenes. However, existing MMIF methods lack the ability to resist different weather interferences in real-life scenarios, preventing them from being useful in practical applications such as autonomous driving. To bridge this research gap, we proposed an all-weather MMIF model. Regarding deep learning architectures, their network designs are often viewed as a black box, which limits their multitasking capabilities. For deweathering module, we propose a physically-aware clear feature prediction module based on an atmospheric scattering model that can deduce variations in light transmittance from both scene illumination and depth. For fusion module, We utilize a learnable low-rank representation model to decompose images into low-rank and sparse components. This highly interpretable feature separation allows us to better observe and understand images. Furthermore, we have established a benchmark for MMIF research under extreme weather conditions. It encompasses multiple scenes under three types of weather: rain, haze, and snow, with each weather condition further subdivided into various impact levels. Extensive fusion experiments under adverse weather demonstrate that the proposed algorithm has excellent detail recovery and multi-modality feature extraction capabilities