A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
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No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.276 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
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The record
- Venue
- IEEE Transactions on Image Processing
- Topic
- Advanced Image Processing Techniques
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Intel Collaboration Research Institute for Computational IntelligenceAzrieli Foundation
- Keywords
- Computer scienceArtificial intelligenceComputational complexity theoryCluster analysisImage (mathematics)Pattern recognition (psychology)Iterative reconstructionResolution (logic)Artificial neural networkAlgorithmImage resolution
- Has abstract in OpenAlex
- yes