RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters

TL;DR

RSFNet is a novel white-box image retouching framework that utilizes region-specific filters with parallel processing, enabling fine-grained, customizable enhancements and achieving state-of-the-art aesthetic results.

Contribution

The paper introduces RSFNet, a white-box retouching model that employs parallel region-specific filters with linear summation, diverging from traditional cascaded global filters.

Findings

RSFNet achieves state-of-the-art aesthetic quality.

RSFNet enables fine-grained, region-specific editing.

RSFNet offers increased user convenience and flexibility.

Abstract

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.