A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Sub-pixel Bayesian estimation of albedo and height
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Super-Resolution Imaging
Local Blur Estimation and Super-Resolution
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Minimal operator set for passive depth from defocus
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Maximum-likelihood depth-from-defocus for active vision
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Superresolution restoration of an image sequence: adaptive filtering approach
IEEE Transactions on Image Processing
Restoration of spatially varying blurred images using multiple model-based extended Kalman filters
IEEE Transactions on Image Processing
Depth Estimation and Image Restoration Using Defocused Stereo Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Super-resolution with integrated radial distortion correction
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Simultaneous estimation of super-resolved depth map and intensity field using photometric cue
Computer Vision and Image Understanding
A fast algorithm for image super-resolution from blurred observations
EURASIP Journal on Applied Signal Processing
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
Simultaneous estimation of super-resolved depth map and intensity field using photometric cue
Computer Vision and Image Understanding
Analysis of multiframe super-resolution reconstruction for image anti-aliasing and deblurring
Image and Vision Computing
Super-resolution using sub-band constrained total variation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Video enhancement using a robust iterative SRR based on Leclerc stochastic estimation
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
A novel method for multi-focus image fusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiframe image restoration in the presence of noisy blur kernel
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
Directionally adaptive single frame image super resolution
International Journal of Innovative Computing and Applications
Super resolution using graph-cut
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Journal of Computational and Applied Mathematics
Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels
Journal of Computational and Applied Mathematics
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This paper presents a novel technique to simultaneously estimate the depth map and the focused image of a scene, both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high spatial resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted mostly to the intensity domain. In this paper, we extend the scope of super-resolution imaging to acquire depth estimates at high spatial resolution simultaneously. Given a sequence of low resolution, blurred, and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true high resolution focused image. Both the depth and the image are modeled as separate Markov random fields (MRF) and a maximum a posteriori estimation method is used to recover the high resolution fields. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution and structure recovery techniques, we do away with the correspondence problem.