Math @ Duke

Publications [#322685] of Guillermo Sapiro
Papers Published
 Delbracio, M; Sapiro, G, Burst deblurring: Removing camera shake through fourier burst accumulation,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0712June2015
(October, 2015),
pp. 23852393, ISBN 9781467369640 [doi]
(last updated on 2018/10/16)
Abstract: © 2015 IEEE. Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently illposed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method's rationale is that camera shake has a random nature and therefore each image in the burst is generally blurred differently. Experiments with real camera data show that the proposed Fourier Burst Accumulation algorithm achieves stateoftheart results an order of magnitude faster, with simplicity for onboard implementation on camera phones.


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