Single Image Dehazing via Multi scale Convolutional Neural Networks
The performance of existing image dehazing methods is limited by hand designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi scale deep neural network for single image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse scale net which predicts a holistic transmission map based on the entire image, and a fine scale net which refines results locally. To train the multi scale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state of the art methods on both synthetic and real world images in terms of quality and speed.
ECCV