Single‐image super resolution using evolutionary sparse coding technique
| Autor: | Kaveh Ahmadi, Ezzatollah Salari |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
K-SVD
Computer science business.industry Image quality ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Image processing Pattern recognition 02 engineering and technology Iterative reconstruction Sparse approximation Signal Processing Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Neural coding business Image resolution Software |
| Zdroj: | IET Image Processing. 11:13-21 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2016.0273 |
| Popis: | Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality. |
| Databáze: | OpenAIRE |
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