RSFNet：一种使用区域特定颜色滤镜的白盒图像修图方法
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters
AI总结：
针对现有白盒图像修图方法无法细粒度调整的问题，提出采用并行区域特定滤镜线性求和的RSFNet框架，同时生成滤镜参数与区域注意力图，实现了更优的修图效果与更高的用户编辑便利性。
摘要：
图像修图是提升照片视觉吸引力的重要环节。尽管用户往往有共同的审美偏好，但他们的修图方式可能因个人喜好而有所不同。因此，需要既能产出令人满意的效果、又能让用户同时便捷编辑图像的白盒方法。近期的白盒修图方法依赖级联全局滤镜，这类滤镜提供图像级别的滤镜参数，但无法进行细粒度修图。相比之下，调色师在使用Davinci Resolve等传统工具时，通常采用分治策略，执行一系列区域特定的细粒度增强操作。我们借鉴这一思路，开发了一种使用并行区域特定滤镜的照片修图白盒框架，名为RSFNet。我们的模型会同时生成每个滤镜的参数（如饱和度、对比度、色相）以及区域注意力图。RSFNet没有采用滤镜级联的方式，而是使用滤镜的线性求和，这使得滤镜类别可以更加多样，且训练难度更低。我们的实验表明，RSFNet取得了当前最优的结果，在可编辑的白盒修图方面提供了令人满意的审美效果和更高的用户便利性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
