Super-resolution through neighbor embedding
2004
1,937 citations
Abstract
In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its highresolution counterpart using a set of training examples. While this formulation resembles other learningbased methods for super-resolution, our method has been inspired by recent manifold learning methods, particularly locally linear embedding (LLE). Specifically, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results. 1.
Keywords
EmbeddingArtificial intelligenceFeature vectorComputer scienceNonlinear dimensionality reductionManifold (fluid mechanics)Pattern recognition (psychology)Image (mathematics)Feature (linguistics)Image resolutionResolution (logic)Computer visionMathematicsDimensionality reduction
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Publication Info
- Year
- 2004
- Type
- article
- Volume
- 1
- Pages
- 275-282
- Citations
- 1937
- Access
- Closed
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Cite This
Hong Chang,
Dit‐Yan Yeung,
Yimin Xiong
(2004).
Super-resolution through neighbor embedding.
, 1
, 275-282.
https://doi.org/10.1109/cvpr.2004.1315043
Identifiers
- DOI
- 10.1109/cvpr.2004.1315043