  A popular method for enhancing images involves learning the style of a
professional photo editor using pairs of training images comprised of the
original input with the editor-enhanced version. When manipulating images, many
editing tools offer a feature that allows the user to manipulate a limited
selection of familiar colors. Editing by color name allows easy adjustment of
elements like the "blue" of the sky or the "green" of trees. Inspired by this
approach to color manipulation, we propose NamedCurves, a learning-based image
enhancement technique that separates the image into a small set of named
colors. Our method learns to globally adjust the image for each specific named
color via tone curves and then combines the images using an attention-based
fusion mechanism to mimic spatial editing. We demonstrate the effectiveness of
our method against several competing methods on the well-known Adobe 5K dataset
and the PPR10K dataset, showing notable improvements.
