Bayesian blind deconvolution from differently exposed image pairs

Authors:
S. Derin Babacan;Jingnan Wang;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
EECS Department, Northwestern University, Evanston, IL;EECS Department, Northwestern University, Evanston, IL;Departamento de Ciencias de la Computación e I.A., Universidad de Granada, Granada, Spain;EECS Department, Northwestern University, Evanston, IL
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
Cited 1

Two motion-blurred images are better than one

Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Removing camera shake from a single photograph

ACM SIGGRAPH 2006 Papers
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Image deblurring with blurred/noisy image pairs

ACM SIGGRAPH 2007 papers
Multichannel blind iterative image restoration

IEEE Transactions on Image Processing
Multichannel blind deconvolution of spatially misaligned images

IEEE Transactions on Image Processing
Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

IEEE Transactions on Image Processing

Bayesian blind deconvolution from differently exposed image pairs

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Photographs acquired under low-light conditions require long exposure times and therefore exhibit significant blurring due to the shaking of the camera. Using shorter exposure times results in sharper images but with a very high level of noise. In this paper we address this problem and present a novel blind deconvolution algorithm for a pair of differently exposed images. We formulate the problem in a hierarchical Bayesian framework by utilizing prior knowledge on the unknown image and blur, and also on the dependency between two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary algorithm parameters along with the unknowns, such that no user-intervention is needed. Moreover, we employ a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates. Experimental results demonstrate the high restoration performance of the proposed algorithm.