  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.
