Unsupervised image translation

An interesting and potentially useful vision/graphics task is to render an input image in an enhanced form or also in an unusual style; for example with increased sharpness or with some artistic qualities. In previous work [10, 5], researchers showed that by estimating the mapping from an input image to a registered (aligned) image of the same scene in a different style or resolution, the mapping could be used to render a new input image in that style or resolution. Frequently a registered pair is not available, but instead the user may have only a source image of an unrelated scene that contains the desired style. In this case, the task of inferring the output image is much more difficult since the algorithm must both infer correspondences between features in the input image and the source image, and infer the unknown mapping between the images. We describe a Bayesian technique for inferring the most likely output image. The prior on the output image P(X) is a patch-based Markov random field obtained from the source image. The likelihood of the input P(Y\X) is a Bayesian network that can represent different rendering styles. We describe a computationally efficient, probabilistic inference and learning algorithm for inferring the most likely output image and learning the rendering style. We also show that current techniques for image restoration or reconstruction proposed in the vision literature (e.g., image super-resolution or de-noising) and image-based non-photorealistic rendering could be seen as special cases of our model. We demonstrate our technique using several tasks, including rendering a photograph in the artistic style of an unrelated scene, de-noising, and texture transfer