Image Restoration from Parametric Transformations using Generative Models
This addresses image restoration problems like inpainting and super-resolution for computer vision applications, offering a more robust and automated solution compared to existing methods.
The paper tackles image restoration under unknown parametric transformations by formulating it as a statistical estimation problem using generative models, achieving a parameter-free optimization method that outperforms state-of-the-art approaches requiring exact transformation knowledge and tuned regularizers.
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. The resulting optimization is completely defined with no parameters requiring tuning. This must be compared with the current state of the art which requires exact knowledge of the transformations and contains regularizer terms with weights that must be properly defined. Finally, we must mention that we extend our method to accommodate mixtures of multiple images where each image is described by its own generative model and we are able of successfully separating each participating image from a single mixture.