Single image Super Resolution by no-reference image quality index optimization in PCA subspace
Principal Component Analysis (PCA) has been effectively applied for solving atmospheric-turbulence degraded images. PCA-based approaches improve the image quality by adding high-frequency components extracted using PCA to the blurred image. The PCA-based restoration process is similar with conventio...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73126/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983517105&doi=10.1109%2fCSPA.2016.7515828&partnerID=40&md5=b67866974e335b257bdbfed1f77276d0 |
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| Summary: | Principal Component Analysis (PCA) has been effectively applied for solving atmospheric-turbulence degraded images. PCA-based approaches improve the image quality by adding high-frequency components extracted using PCA to the blurred image. The PCA-based restoration process is similar with conventional single-frame Super-Resolution (SR) methods, which perform SR process by improving the edges portion of low-resolution images. This paper aims to introduce PCA-based restoration to solve SR problem with additive white Gaussian noise. We conducted experiments using standard image database and show comparative result with the latest deep-learning SR approach. |
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