Wavelet-based single image super-resolution with an overall enhancement procedure
2017
In this paper, we address the problem of generating a super-resolution image based on a dictionary of low- and high-resolution exemplars from a single input image in wavelet domain with a overall enhancement procedure. Most methods extract different kinds of features in low-resolution image and high-resolution images to establish the mapping relation. But in this paper, we implement wavelet-transform to extract the same kind of feature to make the mapping more reasonable. Meanwhile we implement local Lipschitz regularity constraint and structure-keeping constraint to preserve the local singularity and edge in our method. Compared with current state-of-art methods on standard images, our method obtains both visual and PSNR improvement.
Keywords:
- Wavelet transform
- Artificial intelligence
- Mathematical optimization
- Stationary wavelet transform
- Second-generation wavelet transform
- Pattern recognition
- Feature detection (computer vision)
- Image resolution
- Image fusion
- Cascade algorithm
- Computer vision
- Wavelet packet decomposition
- Mathematics
- Computer science
- Discrete wavelet transform
- Wavelet
- Feature extraction
- Correction
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