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This paper proposes a general framework for internal patch-based imagerestoration based on Conditional Random Fields (CRF). Unlike related modelsbased on Markov Random Fields (MRF), our approach explicitly formulates theposterior distribution for the entire image. The potential functions are takenas proportional to the product of a likelihood and prior for each patch. Byassuming identical parameters for similar patches, our approach can beclassified as a model-based non-local method. For the prior term in thepotential function of the CRF model, multivariate Gaussians and multivariatescale-mixture of Gaussians are considered, with the latter being a novel priorfor image patches. Our results show that the proposed approach outperformsmethods based on Gaussian mixture models for image denoising andstate-of-the-art methods for image interpolation/inpainting.

Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians