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
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Sub-pixel Bayesian estimation of albedo and height
International Journal of Computer Vision
Space-variant approaches to recovery of depth from defocused images
Computer Vision and Image Understanding
Motion Deblurring and Super-resolution from an Image Sequence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Local Blur Estimation and Super-Resolution
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
3D super-resolution using generalized sampling expansion
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Super-Resolution Image Restoration from Blurred Low-Resolution Images
Journal of Mathematical Imaging and Vision
Simultaneous estimation of super-resolved depth map and intensity field using photometric cue
Computer Vision and Image Understanding
Single frame image super-resolution: should we process locally or globally?
Multidimensional Systems and Signal Processing
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
Decimation Estimation and Linear Model-Based Super-Resolution Using Zoomed Observations
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
A soft MAP framework for blind super-resolution image reconstruction
Image and Vision Computing
Simultaneous estimation of super-resolved depth map and intensity field using photometric cue
Computer Vision and Image Understanding
Resolution enhancement based on learning the sparse association of image patches
Pattern Recognition Letters
Filtering image sequences from a moving object and the edge detection problem
Computers & Mathematics with Applications
Multidimensional Systems and Signal Processing
Decimation estimation and super-resolution using zoomed observations
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Computer Vision and Image Understanding
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This paper presents a new technique for generating a high resolution image from a blurred image sequence; this is also referred to as super-resolution restoration of images. The image sequence consists of decimated, blurred and noisy versions of the high resolution image. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used for super-resolution restoration. Unlike other super-resolution imaging methods, the proposed technique does not require sub-pixel registration of given observations. A simple gradient descent method is used to optimize the functional. The discontinuities in the intensity process can be preserved by introducing suitable line processes. Superiority of this technique to standard methods of image expansion like pixel replication and spline interpolation is illustrated.