<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schelten, Kevin</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interleaved regression tree field cascades for blind image deconvolution</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">494–501</style></pages><isbn><style face="normal" font="default" size="100%">9781479966820</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image blur from camera shake is a common cause for poor image quality in digital photography, prompting a significant recent interest in image deblurring. The vast majority of work on blind deblurring splits the problem into two subsequent steps: First, the blur process (i.e., blur kernel) is estimated, then the image is restored given the estimated kernel using a non-blind deblurring algorithm. Recent work in non-blind deblurring has shown that discriminative approaches can have clear image quality and runtime benefits over typical generative formulations. In this paper, we propose a cascade for blind deblurring that alternates between kernel estimation and discriminative deblurring using regression tree fields (RTFs). We further contribute a new dataset of realistic image blur kernels from human camera shake, which we use to train the discriminative component. Extensive qualitative and quantitative experiments show a clear gain in image quality by interleaving kernel estimation and discriminative deblurring in an iterative cascade.</style></abstract></record></records></xml>