From Learning Models of Natural Image Patches to Whole Image Restoration

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,...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Daniel Zoran, Yair Weiss
• Keywords: Computational Challenges, Computer Vision, Computer Vision And Image Processing, Gaussian Mixture Model, ICCV 2011