  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.
