Patch complexity, finite pixel correlations and optimal denoising
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Loss-specific training of non-parametric image restoration models: a new state of the art
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Nonlocal spectral prior model for low-level vision
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Proceedings of the first ACM workshop on Information hiding and multimedia security
A no-reference metric for evaluating the quality of motion deblurring
ACM Transactions on Graphics (TOG)
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Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently. This raises three questions - do priors that give high likelihood to the data also lead to good performance in restoration? Can we use such patch based priors to restore a full image? Can we learn better patch priors? In this work we answer these questions. We compare the likelihood of several patch models and show that priors that give high likelihood to data perform better in patch restoration. Motivated by this result, we propose a generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated. We show how to derive an appropriate cost function, how to optimize it and how to use it to restore whole images. Finally, we present a generic, surprisingly simple Gaussian Mixture prior, learned from a set of natural images. When used with the proposed framework, this Gaussian Mixture Model outperforms all other generic prior methods for image denoising, deblurring and inpainting.