Discrete Haze Level Dehazing Network

In contrast to traditional dehazing methods, deep learning based single image dehazing (SID) algorithms have achieved better performances by creating a mapping function from haze to haze-free images. Usually, the images taken from the natural scenes have different haze levels, but deep SID algorithms only process the hazy images as one group. It makes the deep SID algorithms difficult to deal with the image set with some images having specific haze density. In this paper, a Discrete Haze Level Dehazing network (DHL-Dehaze), a very effective method to dehaze multiple different haze level images, is proposed. The proposed approach considers a single image dehazing problem as a multi-domain image-to-image translation, instead of grouping all hazy images into the same domain. DHL-Dehaze provides computational derivation to describe the role of different haze levels for image translation. To verify the proposed approach, we synthesize two largescale datasets with multiple haze level images based on the NYU-Depth and DIML/CVL datasets. The experiments show that DHL-Dehaze can obtain excellent quantitative and qualitative dehazing results, especially when the haze concentration is high.