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High-quality imaging is crucial for ensuring safety supervision andintelligent deployment in fields like transportation and industry. It enablesprecise and detailed monitoring of operations, facilitating timely detection ofpotential hazards and efficient management. However, adverse weatherconditions, such as atmospheric haziness and precipitation, can have asignificant impact on image quality. When the atmosphere contains dense haze orwater droplets, the incident light scatters, leading to degraded capturedimages. This degradation is evident in the form of image blur and reducedcontrast, increasing the likelihood of incorrect assessments andinterpretations by intelligent imaging systems (IIS). To address the challengeof restoring degraded images in hazy and rainy conditions, this paper proposesa novel multi-view knowledge-guided scene recovery network (termed MvKSR).Specifically, guided filtering is performed on the degraded image to separatehigh/low-frequency components. Subsequently, an en-decoder-based multi-viewfeature coarse extraction module (MCE) is used to coarsely extract featuresfrom different views of the degraded image. The multi-view feature fine fusionmodule (MFF) will learn and infer the restoration of degraded images throughmixed supervision under different views. Additionally, we suggest an atrousresidual block to handle global restoration and local repair inhazy/rainy/mixed scenes. Extensive experimental results demonstrate that MvKSRoutperforms other state-of-the-art methods in terms of efficiency and stabilityfor restoring degraded scenarios in IIS.

MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation