NDLI: Discriminative Non-blind Deblurring

Content Provider	IEEE Xplore Digital Library
Author	Schmidt, U. Rother, C. Nowozin, S. Jancsary, J. Roth, S.
Copyright Year	2013
Description	Author affiliation: Microsoft Res. Cambridge, Cambridge, UK (Rother, C.; Nowozin, S.; Jancsary, J.) \|\| Dept. of Comput. Sci., Tech. Univ. Darmstadt, Darmstadt, Germany (Schmidt, U.; Roth, S.)
Abstract	Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a discriminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaussian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.
Starting Page	604
Ending Page	611
File Size	2588501
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.84
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image restoration Kernel Regression tree analysis Training Computational modeling Image denoising Predictive models
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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