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.

翻译：摘要：高质量成像对于交通、工业等领域的安全监控与智能部署至关重要，它能实现精准细致的操作监测，助力潜在危险的及时发现与高效管理。然而，大气雾霾、降水等恶劣天气条件会严重影响图像质量。当大气中含有浓密雾霾或水滴时，入射光发生散射导致采集图像降质，表现为图像模糊和对比度下降，从而增加智能成像系统（IIS）误判与错误解读的风险。为应对雾雨条件下降质图像的恢复难题，本文提出一种新颖的多视角知识引导场景恢复网络（简称MvKSR）。具体而言，首先对降质图像进行引导滤波以分离高/低频分量，随后采用基于编码器-解码器的多视角特征粗提取模块（MCE）从降质图像的不同视角粗提取特征。多视角特征精细融合模块（MFF）则通过不同视角下的混合监督学习推断降质图像的恢复过程。此外，我们提出一种空洞残差模块来处理雾天/雨天/混合场景中的全局恢复与局部修复问题。大量实验结果表明，MvKSR在IIS降质场景恢复的效率和稳定性方面均优于现有最先进方法。