  Scattering and attenuation of light in no-homogeneous imaging media or
inconsistent light intensity will cause insufficient contrast and color
distortion in the collected images, which limits the developments such as
vision-driven smart urban, autonomous vehicles, and intelligent robots. In this
paper, we propose an all-in-one scene recovery network via multi-knowledge
integration (termed AoSRNet) to improve the visibility of imaging devices in
typical low-visibility imaging scenes (e.g., haze, sand dust, and low light).
It combines gamma correction (GC) and optimized linear stretching (OLS) to
create the detail enhancement module (DEM) and color restoration module (CRM).
Additionally, we suggest a multi-receptive field extraction module (MEM) to
attenuate the loss of image texture details caused by GC nonlinear and OLS
linear transformations. Finally, we refine the coarse features generated by
DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image.
Comprehensive experimental results demonstrate the effectiveness and stability
of AoSRNet compared to other state-of-the-art methods. The source code is
available at \url{https://github.com/LouisYuxuLu/AoSRNet}.
