利用中通道补偿的单幅图像去雾

针对雾天图像对比度低和细节模糊等问题，将图像分解为纹理层和结构层，对含有大部分雾气的结构层进行去雾，对纹理层进行增强.为了避免大气光估计易受白色物体影响，提出一种RGB空间立体判决图，并设计基于自适应阈值约束的大气光估计方法，可有效区分天空和非天空区域;针对暗通道先验处理大面积天空、浓雾区域失效问题，提出一种基于中通道补偿的透射率估计方法，可有效克服去雾后图像颜色失真;同时，基于侧窗导向滤波对上述透射率进行修正，能够较好地保持细节.实验表明，本文方法能有效去除雾气，去雾后图像颜色自然，细节保持良好.

Single Image Dehazing Based on Middle Channel Compensation

Focusing on foggy images with low contrast and blurry detail， this paper decomposes the images into texture layer and structural layer firstly. Then the structural layer containing most of the fog is dehazed， and the texture layer is enhanced. To avoid the effect of white objects in the scene on atmospheric light estimation， this paper proposes a kind of stereo decision map in RGB space， and designs an atmospheric light estimation method which can adaptively distinguish between sky and non-sky area based on a threshold constraining scheme. Due to the inefficiency of dark channel prior when dealing with large areas of sky and dense fog， a transmission estimation approach based on middle channel compensation is put forward， which can avoid color distortion of dehazed images. Furthermore， the transmission is refined by the guided filtering with side window， which can preserve image details. Extensive experimental results show that the proposed approach can realize haze removal thoroughly， and the dehazed image has natural colors and vivid details.