Single Image Dehazing using a Novel Histogram Tranformation Network

Images taken outdoor often experience degradation due to the influence of haze. A lot of algorithms have been proposed to solve this problem. One kind of algorithms are based on some hand-crafted features, which often work only in the situations where those hand-crafted features are valid. There are also some algorithms, which use deep learning-based methods to recover clear images, but they depend on the 2D images, and their run time increases rapidly when the size of images gets larger. Moreover, these models need a large dataset to be trained. In this study, we proposed a novel way based on deep learning and histogram matching to overcome these common problems. Firstly, we develop a network with 1D ResNet structure to predict the histogram of a recovered image. Secondly, we match the histograms of the inputs to the outputs of the model, which are processed patch by patch, to get a series of clear patches. Finally, we use an image-guided filter to overcome the unnatural transition between patches. Experiments on both synthetic and real-world hazy images show that our method performs about 3% better in terms of SSIM(structural similarity index) and 15% better in terms of CIEDE2000 than some state-of-the-art methods on a synthetic hazy image dataset. Furthermore, our model runs faster than other deep-learning-based algorithms in our experiments by about 187% to 382%.