  Retouching images is an essential aspect of enhancing the visual appeal of
photos. Although users often share common aesthetic preferences, their
retouching methods may vary based on their individual preferences. Therefore,
there is a need for white-box approaches that produce satisfying results and
enable users to conveniently edit their images simultaneously. Recent white-box
retouching methods rely on cascaded global filters that provide image-level
filter arguments but cannot perform fine-grained retouching. In contrast,
colorists typically employ a divide-and-conquer approach, performing a series
of region-specific fine-grained enhancements when using traditional tools like
Davinci Resolve. We draw on this insight to develop a white-box framework for
photo retouching using parallel region-specific filters, called RSFNet. Our
model generates filter arguments (e.g., saturation, contrast, hue) and
attention maps of regions for each filter simultaneously. Instead of cascading
filters, RSFNet employs linear summations of filters, allowing for a more
diverse range of filter classes that can be trained more easily. Our
experiments demonstrate that RSFNet achieves state-of-the-art results, offering
satisfying aesthetic appeal and increased user convenience for editable
white-box retouching.
