  Retouching can significantly elevate the visual appeal of photos, but many
casual photographers lack the expertise to do this well. To address this
problem, previous works have proposed automatic retouching systems based on
supervised learning from paired training images acquired before and after
manual editing. As it is difficult for users to acquire paired images that
reflect their retouching preferences, we present in this paper a deep learning
approach that is instead trained on unpaired data, namely a set of photographs
that exhibits a retouching style the user likes, which is much easier to
collect. Our system is formulated using deep convolutional neural networks that
learn to apply different retouching operations on an input image. Network
training with respect to various types of edits is enabled by modeling these
retouching operations in a unified manner as resolution-independent
differentiable filters. To apply the filters in a proper sequence and with
suitable parameters, we employ a deep reinforcement learning approach that
learns to make decisions on what action to take next, given the current state
of the image. In contrast to many deep learning systems, ours provides users
with an understandable solution in the form of conventional retouching edits,
rather than just a "black-box" result. Through quantitative comparisons and
user studies, we show that this technique generates retouching results
consistent with the provided photo set.
