Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Efficient Super-Resolution and Applications to Mosaics
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Good continuations in digital image level lines
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Towards a Mathematical Theory of Primal Sketch and Sketchability
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Extraction of high-resolution frames from video sequences
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
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
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We address the problem of single image super-resolution by exploring the manifold properties. Given a set of low resolution image patches and their corresponding high resolution patches, we assume they respectively reside on two non-linear manifolds that have similar locally-linear structure. This manifold correlation can be realized by a three-layer Markov network that connects performing super-resolution with energy minimization. The main advantage of our approach is that by working directly with the network model, there is no need to actually construct the mappings for the underlying manifolds. To achieve such efficiency, we establish an energy minimization model for the network that directly accounts for the expected property entailed by the manifold assumption. The resulting energy function has two nice properties for super-resolution. First, the function is convex so that the optimization can be efficiently done. Second, it can be shown to be an upper bound of the reconstruction error by our algorithm. Thus, minimizing the energy function automatically guarantees a lower reconstruction error— an important characteristic for promising stable super-resolution results.