Making Images from Images: Tightly Constrained Parallel Denoising

We present methods to transform an image into a novel one of any subject matter simply by rearranging the image’s tiles. Our method extends and improves recent work in the generation of optical illusions by discovering the optimal arrangement of the image’s tiles simultaneously with the image generation. In addition to producing images that more accurately represent the subject matter, this technique allows us to address a much broader class of problems than previously possible. By learning the image transforms, we allow any source image to be pre- specified; any existing image (e.g. the Mona Lisa) can be transformed to a novel subject. We formulate this as a tightly constrained optimization problem and address it through alternating the steps of image diffusion and energy minimization using optimal matching. Under our formulation, a simple method to extend this to infinite copies of the source image is also given. Unlike previous methods, as the number of tiles grows the problem becomes easier and the results become better.