Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an input image based on just a single pair of before and after example images. Our approach provides accurate and generalizable detail edit transfer to new images. We achieve these by proposing a new representation for image to image maps. Specifically, we propose neural field based transformation blending in the patch space for defining patch to patch transformations for each frequency band. This parametrization of the map with anchor transformations and associated weights, and spatio-spectral localized patches, allows us to capture details well while staying generalizable. We evaluate our technique both on known ground truth filters and artist retouching edits. Our method accurately transfers complex detail retouching edits.
翻译:照片修饰对于初学者来说是一个困难的任务,因为它需要专业知识和高级工具。摄影师常常花费大量时间制作具有复杂细节的高质量修饰照片。在本文中,我们介绍了一种基于一次性学习的技术,仅基于一对先前和之后的示例图像即可自动修饰输入图像的详细信息。我们的方法提供了准确且实用的详细信息编辑转换至新图像。通过提出新的图像映射表示法,我们实现了这一点。具体而言,我们建议在修补材料空间中采用基于神经场的变换混合法用于每个频带的修补变换。锚定变换和相关权重以及局部时空频域的修补等参数化方法使我们能够准确捕获细节并保持通用性。我们评估了我们的技术,并与已知的完美过滤器和艺术家修饰所进行的编辑作出了精确的比较。我们的方法能准确地转换非常复杂的细节编辑。