  High-quality imaging is crucial for ensuring safety supervision and
intelligent deployment in fields like transportation and industry. It enables
precise and detailed monitoring of operations, facilitating timely detection of
potential hazards and efficient management. However, adverse weather
conditions, such as atmospheric haziness and precipitation, can have a
significant impact on image quality. When the atmosphere contains dense haze or
water droplets, the incident light scatters, leading to degraded captured
images. This degradation is evident in the form of image blur and reduced
contrast, increasing the likelihood of incorrect assessments and
interpretations by intelligent imaging systems (IIS). To address the challenge
of restoring degraded images in hazy and rainy conditions, this paper proposes
a novel multi-view knowledge-guided scene recovery network (termed MvKSR).
Specifically, guided filtering is performed on the degraded image to separate
high/low-frequency components. Subsequently, an en-decoder-based multi-view
feature coarse extraction module (MCE) is used to coarsely extract features
from different views of the degraded image. The multi-view feature fine fusion
module (MFF) will learn and infer the restoration of degraded images through
mixed supervision under different views. Additionally, we suggest an atrous
residual block to handle global restoration and local repair in
hazy/rainy/mixed scenes. Extensive experimental results demonstrate that MvKSR
outperforms other state-of-the-art methods in terms of efficiency and stability
for restoring degraded scenarios in IIS.
