Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach for color enhancement that models the process as a sequence of global adjustments, using a novel 'distort-and-recover' training scheme that only requires high-quality images.

Contribution

It proposes a DRL-based method for color enhancement with a new training scheme that avoids needing paired input-retouched images, improving interpretability and training efficiency.

Findings

Produces competitive color enhancement results

More suitable for 'distort-and-recover' training than supervised methods

Effective in modeling step-wise human retouching process

Abstract

Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images or produce results in a non-interpretable way. In this paper, we present a deep reinforcement learning (DRL) based method for color enhancement to explicitly model the step-wise nature of human retouching process. We cast a color enhancement process as a Markov Decision Process where actions are defined as global color adjustment operations. Then we train our agent to learn the optimal global enhancement sequence of the actions. In addition, we present a 'distort-and-recover' training scheme which only requires high-quality reference images for training instead of input and retouched image pairs. 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 and our DRL approach is more suitable for the 'distort-and-recover' training scheme than previous supervised approaches. Supplementary material and code are available at https://sites.google.com/view/distort-and-recover/