Interleaved regression tree field cascades for blind image deconvolution

TitleInterleaved regression tree field cascades for blind image deconvolution
Publication TypeConference Paper
Year of Publication2015
AuthorsSchelten, K, Nowozin, S, Jancsary, J, Rother, C, Roth, S
Conference NameProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
ISBN Number9781479966820

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.

Citation KeySchelten2015