Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SoftCuts: a soft edge smoothness prior for color image super-resolution
IEEE Transactions on Image Processing
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
High resolution image formation from low resolution frames using Delaunay triangulation
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Joint Learning for Single-Image Super-Resolution via a Coupled Constraint
IEEE Transactions on Image Processing
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This paper proposes a new high speed Single Image Super Resolution algorithm and also suggests modifications that can perform super resolution on video sequences. Embarking from recent successful algorithms proposed by Yang et. al.[18] and Elad et. al.[19], it adds a number of enhancements that improve both PSNR of the recovered image and performance of the dictionary training. It also proposes an incremental dictionary update strategy that enhances results on video sequences by improving the dictionary quality at each frame. The algorithm does not necessarily need a training image set, though it can use one to enhance PSNR of the upscaled image. It automatically picks the patches that would benefit from super resolution, ignoring others, thus enhancing speed. It also partially accounts for spatial transformations of patches in the trained dictionary, further enhancing performance.