Learning-based color enhancement approaches typically learn to map from input images to retouched images. Previous methods do not explicitly model step-by-step human retouching processes and usually require expensive pairs of input-retouched image pairs. In this paper, we present a deep reinforcement learning based method for color enhancement. We formulate a color enhancement process as a Markov Decision Process where actions are defined as global color adjustment operations and learn the optimal global enhancement sequence using deep reinforcement learning. In addition, we present a ‘distort-and-recover’ training scheme which only requires high quality reference images for training instead of input and retouched pair images. Given high-quality reference images, we distort the images’ color distribution and form distorted-reference image pairs for training. Through extensive experiments, we show that our method produces decent enhancement results compared to previous methods and our deep reinforcement learning approach is more suitable for the ‘distort-and-recover’ training scheme than previous supervised learning approaches.
Please contact Jongchan Park (jcpark@lunit.io) for further inquiries.