Exposure：一个白盒照片后处理框架
Exposure: A White-Box Photo Post-Processing Framework
摘要：
修饰可以显著提升照片的视觉吸引力，但许多休闲摄影师缺乏做好这一点的专业知识。为了解决这个问题，以前的作品已经提出了自动润饰系统的基础上监督学习从配对的训练图像之前和之后手动编辑。由于用户很难获得反映其修饰偏好的配对图像，因此本文提出了一种深度学习方法，该方法使用非配对数据进行训练，即一组呈现用户喜欢的修饰风格的照片，这更容易收集。我们的系统使用深度卷积神经网络来制定，该网络学习对输入图像应用不同的修饰操作。通过以统一的方式将这些修饰操作建模为分辨率无关的可微分滤波器，来实现关于各种类型的编辑的网络训练。为了以适当的顺序和适当的参数应用过滤器，我们采用了一种深度强化学习方法，该方法可以根据图像的当前状态来决定下一步采取什么行动。与许多深度学习系统相比，我们的系统以传统修饰编辑的形式为用户提供了一个可理解的解决方案，而不仅仅是一个“黑盒”结果。通过定量比较和用户研究，我们表明，这种技术产生的修饰结果与所提供的照片集一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
