@conference {Schelten2015,
	title = {Interleaved regression tree field cascades for blind image deconvolution},
	booktitle = {Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015},
	year = {2015},
	pages = {494{\textendash}501},
	abstract = {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.},
	isbn = {9781479966820},
	doi = {10.1109/WACV.2015.72},
	author = {Schelten, Kevin and Nowozin, Sebastian and Jancsary, Jeremy and Carsten Rother and Roth, Stefan}
}
