Bayesian blind deconvolution from differently exposed image pairs

Authors:
Sevket Derin Babacan;Jingnan Wang;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign, Champaign, IL and Department of Electrical Engineering and Computer Science, Northwestern Universi ...;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL;Departamento de Ciencias de la Computación e I.A., Universidad de Granada, Spain;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Venue:
IEEE Transactions on Image Processing
Year:
2010

Citing 20
Cited 2

Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Recovering high dynamic range radiance maps from photographs

Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Two motion-blurred images are better than one

Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
A Non-Local Algorithm for Image Denoising

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
High-quality motion deblurring from a single image

ACM SIGGRAPH 2008 papers
Bayesian Methods for Image Super-Resolution

The Computer Journal
Superresolution with compound Markov random fields via the variational EM algorithm

Neural Networks
Variational Bayesian blind deconvolution using a total variation prior

IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Motion deblurring using hybrid imaging

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Multichannel blind iterative image restoration

IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain

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

IEEE Transactions on Image Processing
Optimal Spatial Adaptation for Patch-Based Image Denoising

IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

IEEE Transactions on Image Processing
A Unified Approach to Superresolution and Multichannel Blind Deconvolution

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

IEEE Transactions on Image Processing

Full Length Article: Space-variant blur deconvolution and denoising in the dual exposure problem

Information Fusion
Shape from Sharp and Motion-Blurred Image Pair

International Journal of Computer Vision

Quantified Score

Hi-index	0.01

Visualization

Abstract

Photographs acquired under low-lighting 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 the problem of utilizing two such images in order to obtain an estimate of the original scene and present a novel blind deconvolution algorithm for solving it. 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 the two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary model parameters along with the unknown image and blur, 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 with synthetic and real images demonstrate that the proposed method provides very high quality restoration results and compares favorably to existing methods even though no user supervision is needed.