LAUSR

Influenced by glowing effects, nighttime haze removal is a challenging ill-posed task. Existing nighttime dehazing methods usually result in glowing artifacts, color shifts, overexposure, and noise amplification. Thus, through statistical and theoretical analyses, we propose a simple and effective gray haze-line prior (GHLP) to identify accurate hazy feature areas. This prior demonstrates that haze is concentrated on the haze line in the RGB color space and can be accurately projected into the gray component in the Y channel of the YUV color space. Based on this prior, we establish a new unified nighttime haze removal framework and then decompose a nighttime hazy image into color and gray components in the YUV color space. Glowing color correction and haze removal are two important consecutive steps in the nighttime dehazing process. The glowing color correction method is designed to separately remove glow in the color component and enhance illumination in the gray component. After obtaining a refined nighttime hazy image, we propose a new structure-aware variational framework to simultaneously estimate the inverted scene radiance and the transmission in the gray component. This approach can not only recover the high-quality nighttime scene radiance but also preserve the significant structural information and intrinsic color of the scene. Quantitative and qualitative comparisons validate the excellent effectiveness of the proposed nighttime dehazing method against previous state-of-the-art methods. In addition, the proposed approach can be extended to achieve image enhancement for inclement weather scenes, such as sandstorm scenes and extreme daytime hazy scenes.