抄録
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.
本文言語 | English |
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ホスト出版物のタイトル | Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ページ | 183-187 |
ページ数 | 5 |
ISBN(電子版) | 9781467387804 |
DOI | |
出版ステータス | Published - 2016 7 18 |
イベント | 12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016 - Melaka, Malaysia 継続期間: 2016 3 4 → 2016 3 6 |
Other
Other | 12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016 |
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Country | Malaysia |
City | Melaka |
Period | 16/3/4 → 16/3/6 |
ASJC Scopus subject areas
- Signal Processing
- Control and Systems Engineering
- Control and Optimization