Development of robust neighbor embedding based super-resolution scheme
| Autor: | Deepasikha Mishra, Banshidhar Majhi, Ratnakar Dash, Pankaj Kumar Sa |
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| Rok vydání: | 2016 |
| Předmět: |
Similarity (geometry)
business.industry Cognitive Neuroscience ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Histogram matching 020206 networking & telecommunications Pattern recognition 02 engineering and technology Iterative reconstruction Computer Science Applications Image (mathematics) Artificial Intelligence Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business Robust principal component analysis Selection (genetic algorithm) Mathematics |
| Zdroj: | Neurocomputing. 202:49-66 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2016.04.013 |
| Popis: | In this paper, we propose a robust neighbor embedding super-resolution (RNESR) scheme to generate a super-resolution (SR) image from a single low-resolution (LR) image. It utilizes histogram matching for selection of best training pair of images. This helps to learn co-occurrence prior to high-resolution (HR) image reconstruction. The global neighborhood size is computed from local neighborhood size, which avoids the over-fitting and under-fitting problem during neighbor embedding. Robust locally linear embedding (RLLE) is used in place of locally linear embedding (LLE) to generate HR image. To validate the scheme, exhaustive simulation has been carried out on standard images. Comparative analysis with respect to different measures like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) reveals that the RNESR scheme generates high-quality SR image from a LR image as compared to existing schemes. |
| Databáze: | OpenAIRE |
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