Deep Photo Enhancer: Unpaired Learning for Image Enhancement fromPhotographs with GANs
Abstract
This paper proposes an unpaired learning method for
image enhancement. Given a set of photographs with the
desired characteristics, the proposed method learns a photo
enhancer which transforms an input image into an enhanced image with those characteristics. The method is
based on the framework of two-way generative adversarial networks (GANs) with several improvements. First, we
augment the U-Net with global features and show that it
is more effective. The global U-Net acts as the generator in our GAN model. Second, we improve Wasserstein
GAN (WGAN) with an adaptive weighting scheme. With
this scheme, training converges faster and better, and is less
sensitive to parameters than WGAN-GP. Finally, we propose to use individual batch normalization layers for generators in two-way GANs. It helps generators better adapt to
their own input distributions. All together, they significantly
improve the stability of GAN training for our application.
Both quantitative and visual results show that the proposed
method is effective for enhancing images.