  This paper introduces a divide-and-conquer inspired adversarial learning
(DACAL) approach for photo enhancement. The key idea is to decompose the photo
enhancement process into hierarchically multiple sub-problems, which can be
better conquered from bottom to up. On the top level, we propose a
perception-based division to learn additive and multiplicative components,
required to translate a low-quality image or video into its high-quality
counterpart. On the intermediate level, we use a frequency-based division with
generative adversarial network (GAN) to weakly supervise the photo enhancement
process. On the lower level, we design a dimension-based division that enables
the GAN model to better approximates the distribution distance on multiple
independent one-dimensional data to train the GAN model. While considering all
three hierarchies, we develop multiscale and recurrent training approaches to
optimize the image and video enhancement process in a weakly-supervised manner.
Both quantitative and qualitative results clearly demonstrate that the proposed
DACAL achieves the state-of-the-art performance for high-resolution image and
video enhancement.
