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Photo retouching is a difficult task for novice users as it requires expertknowledge and advanced tools. Photographers often spend a great deal of timegenerating high-quality retouched photos with intricate details. In this paper,we introduce a one-shot learning based technique to automatically retouchdetails of an input image based on just a single pair of before and afterexample images. Our approach provides accurate and generalizable detail edittransfer to new images. We achieve these by proposing a new representation forimage to image maps. Specifically, we propose neural field based transformationblending in the patch space for defining patch to patch transformations foreach frequency band. This parametrization of the map with anchortransformations and associated weights, and spatio-spectral localized patches,allows us to capture details well while staying generalizable. We evaluate ourtechnique both on known ground truth filters and artist retouching edits. Ourmethod accurately transfers complex detail retouching edits.

One-shot Detail Retouching with Patch Space Neural Transformation Blending