{"id":151932,"date":"2005-10-01T00:00:00","date_gmt":"2005-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/patch-based-blind-image-super-resolution\/"},"modified":"2018-10-16T19:56:56","modified_gmt":"2018-10-17T02:56:56","slug":"patch-based-blind-image-super-resolution","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/patch-based-blind-image-super-resolution\/","title":{"rendered":"Patch Based Blind Image Super Resolution"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. We show that in this framework, the estimation of the LR image formation parameters is straightforward. The whole framework is implemented via an annealed Gibbs sampling method. Experiments on SR on both single image and image sequence input show that the proposed method provides an automatic and stable way to compute superresolution and the achieved result is encouraging for both synthetic and real LR images.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic 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