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.

本研究提出了一种基于深度学习的自动修饰系统，该系统使用强化学习方法学习如何根据用户喜好风格对输入图片进行不同的修饰操作，从而避免了获取成对的训练数据。与其他深度学习系统不同的是，该系统提供了可理解的修饰结果，而不仅仅是黑盒子结果，定量比较和用户研究证明该技术的修饰结果与提供的照片集相一致。

曝光：白盒照片后处理框架