Abstract
We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.
Keywords
Robustness (evolution)Artificial intelligenceComputer scienceComputer visionDeformation (meteorology)Matching (statistics)Image resolutionImage (mathematics)Function (biology)Pattern recognition (psychology)MathematicsGeology
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Publication Info
- Year
- 2014
- Type
- article
- Volume
- 2014
- Pages
- 2917-2924
- Citations
- 122
- Access
- Closed
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Cite This
Yu Zhu,
Shuicheng Yan,
Alan Yuille
(2014).
Single Image Super-resolution Using Deformable Patches.
, 2014
, 2917-2924.
https://doi.org/10.1109/cvpr.2014.373
Identifiers
- DOI
- 10.1109/cvpr.2014.373