Uneven Image Dehazing by Heterogeneous Twin Network

Abstract

Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. Although the atmospheric scattering model (ASM) is widely used to describe the formation of hazy images, it is hard to deal with the uneven haze image, once the ASM is restricted with the assumption that the atmosphere distributed homogeneously. This paper analyzes the haze imaging mechanism, then proposes an image-to-image architecture to handle the uneven image dehazing, in which a heterogeneous twin network (HT-Net) with two parallel sub-networks are constructed to establish the high dimensional nonlinear mapping model between the hazy and clean images. Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy image to compensate the edge and texture of the object. The effectiveness is validated by the experiments based on three real haze image datasets which depict the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. One professional uneven haze image dataset is found in the real environment and covers 190 types of scene and 21975 uneven images accordingly. This dataset includes thin, heavy, and uneven haze images. The other two benchmark datasets are I-HAZE and O-HAZE, respectively including 35 pairs of indoor real haze and haze-free (ground-truth) images and 45 different outdoor scenes. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze and achieve superior performance over the other mentioned methods.

Keywords

• Uneven image dehazing
• restoration
• atmospheric scattering model (ASM)
• deep learning
• encoder-decoder