Recurrent alignment with dynamic query attention transformer framework for rainy video restoration

High-quality video is a prerequisite for dependable analysis in surveillance, traffic monitoring, and autonomous navigation. Rain, fog, and haze mask scene structure and reduce detection, tracking, and recognition accuracy. These effects appear in both day and night footage, yet many prior methods are built for one setting and transfer poorly. We address this gap with a single framework that restores multi-weather videos across day and night. It has two parts: (i) the Dynamic Query Attention Transformer (DQAT) block, which uses dynamic convolutions to form adaptive queries and focus attention on informative regions; and (ii) the Temporal Alignment Block (TAB), which enforces flow-free temporal consistency using self-calibrated and deformable convolutions for alignment. Together, they recover fine detail while keeping frames stable under changing weather and illumination. Experiments on day and night multi-weather datasets show consistent gains over recent methods, with a compact design for practical deployment.