In this study, we address local photo enhancement to improve the aesthetic quality of an input image by applying different effects to different regions. Existing photo enhancement methods are either not content-aware or not local; therefore, we propose a crowd-powered local enhancement method for content-aware local enhancement, which is achieved by asking crowd workers to locally optimize parameters for image editing functions. To make it easier to locally optimize the parameters, we propose an active learning based local filter. The parameters need to be determined at only a few key pixels selected by an active learning method, and the parameters at the other pixels are automatically predicted using a regression model. The parameters at the selected key pixels are independently optimized, breaking down the optimization problem into a sequence of single-slider adjustments. Our experiments show that the proposed filter outperforms existing filters, and our enhanced results are more visually pleasing than the results by the existing enhancement methods. Our source code and results are available at https://github.com/satoshi-kosugi/crowd-powered.

翻译：本研究针对局部照片增强问题，通过在不同区域应用不同效果来提升输入图像的美学质量。现有照片增强方法要么不具备内容感知能力，要么不涉及局部处理；为此，我们提出一种基于众包的局部增强方法，通过邀请众包工作者对图像编辑函数的参数进行局部优化来实现内容感知的局部增强。为简化局部参数优化过程，我们提出一种基于主动学习的局部滤波器。通过主动学习方法仅需确定少量关键像素点的参数，其余像素点参数则通过回归模型自动预测。所选关键像素点的参数可独立优化，从而将优化问题分解为一系列单滑块调节操作。实验表明，所提滤波器优于现有滤波器，且我们的增强结果比现有增强方法在视觉上更令人愉悦。源代码和实验结果已发布于 https://github.com/satoshi-kosugi/crowd-powered。