Enhancing Bayesian estimators for removing camera shake

Chao Wang
,
Yong Yue
,
Feng Dong
,
Yubo Tao
,
Xiangyin Ma
,
Gordon Clapworthy

Zhejiang University
,
University of Bedfordshire
,
University of Lincoln

Research Output: Contribution to journal Article Peer-review

Abstract

The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k). MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k), although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter-scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into MAP(k). Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state-of-the-art methods.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 113-125

Journal (Volume, Issue Number)

Computer Graphics Forum (Volume 32, Issue 6)

Publication milestones

Published - 01/01/2013

Publication status

Published - 01/01/2013

ISSN

0167-7055

External Publication IDs

handle.net: 10547/337005
Scopus: 84884284620

Access to documents

10.1111/cgf.12074

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Metrics

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