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Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database

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Köhler,  R
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Hirsch,  M
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Mohler,  BJ
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schoelkopf,  B.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Harmeling,  S
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Köhler, R., Hirsch, M., Mohler, B., Schoelkopf, B., & Harmeling, S. (2012). Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Computer Vision - ECCV 2012 (pp. 27-40). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/21.11116/0000-0001-8B3F-D
Abstract
Motion blur due to camera shake is one of the predominant sources of degradation in handheld photography. Single image blind deconvolution (BD) or motion deblurring aims at restoring a sharp latent image from the blurred recorded picture without knowing the camera motion that took place during the exposure. BD is a long-standing problem, but has attracted much attention recently, cumulating in several algorithms able to restore photos degraded by real camera motion in high quality. In this paper, we present a benchmark dataset for motion deblurring that allows quantitative performance evaluation and comparison of recent approaches featuring non-uniform blur models. To this end, we record and analyse real camera motion, which is played back on a robot platform such that we can record a sequence of sharp images sampling the six dimensional camera motion trajectory. The goal of deblurring is to recover one of these sharp images, and our dataset contains all information to assess how closely various algorithms approximate that goal. In a comprehensive comparison, we evaluate state-of-the-art single image BD algorithms incorporating uniform and non-uniform blur models.