Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Super-Resolution from Image Sequences - A Review
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Based Blind Image Super Resolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Conditional Random Field Model for Video Super-resolution
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Jitter camera: high resolution video from a low resolution detector
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Super resolution using graph-cut
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Bayesian approach to image expansion for improved definition
IEEE Transactions on Image Processing
A shrinkage learning approach for single image super-resolution with overcomplete representations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Variational method for super-resolution optical flow
Signal Processing
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
Single image super-resolution based on space structure learning
Pattern Recognition Letters
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Example-based image super-resolution techniques model the co-occurrence patterns between the middle and high frequency layers of example images to estimate the missing high frequency component for low resolution input. However, many existing approaches seek to estimate the optimal solution within a small set of candidates by using empirical criteria. Hence their representational performance is limited by the quality of the candidate set, and the generated super-resolution image is unstable, with noticeable artifacts. In this paper, we propose a novel image super-resolution method based on learning the sparse association between input image patches and the example image patches. We improve an existing sparse-coding algorithm to find sparse association between image patches. We also propose an iterative training strategy to learn a redundancy reduced basis set to speed up the super-resolution process. Comparing to existing example-based approaches, the proposed method significantly improves image quality, and the produced super-resolution images are sharp and natural, with no obvious artifact.