We introduce a method for fully automatic touch-up of face images by making inferences about the structure of the scene and undesirable textures in the image. A distribution over image segmentations and labelings is computed via a conditional random field; this distribution controls the application of various local image transforms to regions in the image. Parameters governing both the labeling and transforms are jointly optimized w.r.t. a training set of before-and-after example images. One major advantage of our formulation is the ability to approximately marginalize over all possible labelings and thus exploit much or most of the information in the distribution; this yields better results than MAP inference. We demonstrate with a system that is trained to correct red-eye, reduce specularities, and remove acne and other blemishes from faces, showing results with test images scavenged from acne-themed internet message boards.
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