Enhanced Pix2pix Dehazing Network
In this paper, we reduce the image dehazing problem to an image to image translation problem, and propose Enhanced Pix2pix Dehazing Network (EPDN) which generates a haze free image without relying on the physical scattering model. EPDN is embedded by a generative adversarial network, which is followed by a well designed enhancer. Inspired by visual perception global first theory, the discriminator guides the generator to create a pseudo realistic image on a coarse scale, while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale. The enhancer contains two enhancing blocks based on the receptive field model, which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trained with the enhancer. Extensive experiment results on synthetic datasets and real world datasets show that the proposed EPDN is superior to the state of the art methods in terms of PSNR, SSIM, PI, and subjective visual effect.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)