A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Edge Direction Preserving Image Zooming: A Mathematical and Numerical Analysis
SIAM Journal on Numerical Analysis
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Super-Resolution in Medical Imaging
The Computer Journal
A multi-frame image super-resolution method
Signal Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality preserving constraints for super-resolution with neighbor embedding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Low-resolution gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
Single-Image Super-Resolution via Sparse Coding Regression
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
New edge-directed interpolation
IEEE Transactions on Image Processing
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
An edge-guided image interpolation algorithm via directional filtering and data fusion
IEEE Transactions on Image Processing
A New Orientation-Adaptive Interpolation Method
IEEE Transactions on Image Processing
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation
IEEE Transactions on Image Processing
Subpixel edge localization and the interpolation of still images
IEEE Transactions on Image Processing
Zernike-Moment-Based Image Super Resolution
IEEE Transactions on Image Processing
Joint Learning for Single-Image Super-Resolution via a Coupled Constraint
IEEE Transactions on Image Processing
A robust elastic net approach for feature learning
Journal of Visual Communication and Image Representation
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Based on the assumption about the sparse representation of natural images and the theory of compressed sensing, very promising results about single-image super-resolution were obtained by an excellent algorithm introduced by Yang et al. [45]. However, their success could not be well explained theoretically. The lack of theoretical insight has hindered the further improvement of the algorithm. In this paper, Yang's algorithm is revisited in the view of learning theory. According to this point, Yang's algorithm can be considered as a linear regression method in a special feature space which is named as sparse coding space by us. In fact, it has been shown that Yang's algorithm is a result of optimal linear estimation in sparse coding space. More importantly, our theoretical analysis suggests that Yang's algorithm can be improved by using more flexible regression methods than the linear regression method. Following the idea, a novel single-image super-resolution algorithm which is designed based on the framework of L"2-Boosting is proposed in the paper. The experimental results show the effectiveness of the proposed algorithm by comparing with other methods, which verify our theoretical analysis about Yang's algorithm.