Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image restoration using an estimated Markov model
Signal Processing
An Investigation of Methods for Determining Depth from Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
A Variational Approach to Recovering Depth From Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-variant approaches to recovery of depth from defocused images
Computer Vision and Image Understanding
International Journal of Computer Vision
Active Computer Vision by Cooperative Focus and Stereo
Active Computer Vision by Cooperative Focus and Stereo
On a Parameter Estimation Method for Gibbs-Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Selection of Camera Parameters for Recovery of Depth from Defocused Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Maximum Likelihood Estimation of Blur from Multiple Observations
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A block shift-variant blur model for recovering depth from defocused images
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Focused image recovery from two defocused images recorded with different camera settings
IEEE Transactions on Image Processing
A recursive algorithm for maximum likelihood-based identification of blur from multiple observations
IEEE Transactions on Image Processing
A Wavelet Multiresolution Edge Analysis Method for Recovery of Depth from Defocused Images
WAA '01 Proceedings of the Second International Conference on Wavelet Analysis and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active refocusing of images and videos
ACM SIGGRAPH 2007 papers
International Journal of Computer Vision
Augmented reality based on estimation of defocusing and motion blurring from captured images
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Restoration of color images degraded by space-variant motion blur
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Automatic micro-manipulation based on visual servoing
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
Rational filter design for depth from defocus
Pattern Recognition
Iterative feedback estimation of depth and radiance from defocused images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Depth from defocus (DFD) problem involves calculating the depth of various points in a scene by modeling the effect that the focal parameters of the camera have on images acquired with a small depth of field. In this paper, we propose a MAP-MRF-based scheme for recovering the depth and the focused image of a scene from two defocused images. The space-variant blur parameter and the focused image of the scene are both modeled as MRFs and their MAP estimates are obtained using simulated annealing. The scheme is amenable to the incorporation of smoothness constraints on the spatial variations of the blur parameter as well as the scene intensity. It also allows for inclusion of line fields to preserve discontinuities. The performance of the proposed scheme is tested on synthetic as well as real data and the estimates of the depth are found to be better than that of the existing window-based DFD technique. The quality of the space-variant restored image of the scene is quite good even under severe space-varying blurring conditions.