Image Restoration from Parametric Transformations using Generative Models
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When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc. With the help of the generative model it is possible to formulate, in a natural way, these restoration problems as Statistical estimation problems. Our approach, by combining maximum a-posteriori probability with maximum likelihood estimation, is capable of restoring images that are distorted by transformations even when the latter contain unknown parameters. This must be compared with the current state of the art which requires exact knowledge of the transformations. We should also mention that our method does not contain any regularizer terms with unknown weights that need to be properly selected, as is common practice in all recent generative image restoration techniques. Finally, we extend our method to accommodate combinations of multiple images where each image is described by its own generative model and the participating images are being separated from a single combination.
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