Motion Deblurring Using Non-stationary Image Modeling
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
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.
Blind motion deblurring, Camera shake, Blur kernel estimation, Expectation-maximization, Split Bregman iteration
Software Engineering Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-169335DOI: 10.1007/s10851-014-0537-9ISI: 000353563400004ScopusID: 2-s2.0-84928791820OAI: oai:DiVA.org:kth-169335DiVA: diva2:820810