Single-image night haze removal based on color channel transfer and estimation of spatial variation in atmospheric light  

作　　者：Shu-yun Liu Qun Hao Yu-tong Zhang Feng Gao Hai-ping Song Yu-tong Jiang Ying-sheng Wang Xiao-ying Cui Kun Gao 

机构地区：[1]Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,School of Optics and Photonics,Beijing Institute of Technology,Beijing,100081,China [2]China North Vehicle Research Institute,Beijing,China

出　　处：《Defence Technology（防务技术）》2023年第7期134-151,共18页Defence Technology

基　　金：supported by a grant from the Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology (Grant No. GZZKFJJ2020004);the National Natural Science Foundation of China (Grant Nos. 61875013 and 61827814);the Natural Science Foundation of Beijing Municipality (Grant No. Z190018)。

摘　　要：The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.

关 键 词：Dehazing image captured at night Chromaticity fusion correction Color channel transfer Spatial change-based atmospheric light ESTIMATION DehazeNet 

分 类 号：E91[军事] TP391.41[自动化与计算机技术—计算机应用技术]