Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks involve training separate models for automatic cropping or color enhancement in a fully-supervised manner, which requires expensive annotations in the form of image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement parameters for tasks including image cropping and color enhancement. The full differentiability of our image enhancement modules enables training the proposed EnhanceGAN in an end-toend manner. A novel stage-wise learning scheme is further proposed to stabilize the training of each enhancement task and facilitate the extensibility for other image enhancement techniques. Our weakly-supervised EnhanceGAN reports competitive quantitative results against supervised models in automatic image cropping using standard benchmarking datasets, and a user study confirms that the images enhancement results are on par with or even preferred over professional enhancement.