Sumali, B. and Sarkan, H. and Hamada, N. and Mitsukura, Y. (2016) Single image Super Resolution by no-reference image quality index optimization in PCA subspace. In: 12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016, 4 March 2016 through 6 March 2016, Melaka; Malaysia.
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Image Quality Assessment, Noise Robustness, Principal Component Analysis, Single Image, Super Resolution |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Malaysia-Japan International Institute of Technology |
| ID Code: | 73126 |
| Deposited By: | Muhammad Atiff Mahussain |
| Deposited On: | 29 Nov 2017 23:58 |
| Last Modified: | 29 Nov 2017 23:58 |
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