Single Image Haze Removal by Feature Mapping
Yu Zhou, Feiniu Yuan, Xue Xia, Ya Li · 2019
To restore a clear image from a single hazy image, many priors have been proposed for transmission estimation, but these priors often fail in some cases. In this paper, we propose a regression mapping model based on hazy features for haze removal. We fisrt propose sixteen hazy features for mapping, which include the dark channel prior, hazy measures and statistical features. Support Vector Regression (SVR) is adopted to learn the mapping between these hazy features and corresponding transmissions. Then we randomly select patches of synthetic haze and corresponding transmissions from a synthetic dataset to train the mapping model. Experimental results on both natural and synthetic images show that our regression based method achieves significantly better performance than existing state-of-the-art methods.