Change search
ReferencesLink to record
Permanent link

Direct link
Motion Deblurring Using Non-stationary Image Modeling
Show others and affiliations
2015 (English)In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 52, no 2, 234-248 p.Article in journal (Refereed) Published
Abstract [en]

It is well-known that shaken cameras or mobile phones during exposure usually lead to motion blurry photographs. Therefore, camera shake deblurring or motion deblurring is required and requested in many practical scenarios. The contribution of this paper is the proposal of a simple yet effective approach for motion blur kernel estimation, i.e., blind motion deblurring. Though there have been proposed severalmethods formotion blur kernel estimation in the literature, we impose a type of non-stationary Gaussian prior on the gradient fields of sharp images, in order to automatically detect and purse the salient edges of images as the important clues to blur kernel estimation. On one hand, the prior is able to promote sparsity inherited in the non-stationarity of the precision parameters (inverse of variances). On the other hand, since the prior is in a Gaussian form, there exists a great possibility of deducing a conceptually simple and computationally tractable inference scheme. Specifically, the well-known expectation-maximization algorithm is used to alternatingly estimate the motion blur kernels, the salient edges of images as well as the precision parameters in the image prior. In difference from many existing methods, no hyperpriors are imposed on any parameters in this paper; there are not any pre-processing steps involved in the proposed method, either, such as explicit suppression of random noise or prediction of salient edge structures. With estimated motion blur kernels, the deblurred images are finally generated using an off-the-shelf non-blind deconvolution method proposed by Krishnan and Fergus (Adv Neural Inf Process Syst 22:1033-1041, 2009). The rationality and effectiveness of our proposed method have been well demonstrated by the experimental results on both synthetic and realistic motion blurry images, showing state-of-the-art blind motion deblurring performance of the proposed approach in the term of quantitative metric as well as visual perception.

Place, publisher, year, edition, pages
2015. Vol. 52, no 2, 234-248 p.
Keyword [en]
Blind motion deblurring, Camera shake, Blur kernel estimation, Expectation-maximization, Split Bregman iteration
National Category
Software Engineering Computer Science
URN: urn:nbn:se:kth:diva-169335DOI: 10.1007/s10851-014-0537-9ISI: 000353563400004ScopusID: 2-s2.0-84928791820OAI: diva2:820810
Available from: 2015-06-12 Created: 2015-06-12 Last updated: 2015-06-12Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Li, Haibo
By organisation
Media Technology and Interaction Design, MID
In the same journal
Journal of Mathematical Imaging and Vision
Software EngineeringComputer Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 89 hits
ReferencesLink to record
Permanent link

Direct link