Single image Super Resolution by no-reference image quality index optimization in PCA subspace

Brian Sumali, Haslina Sarkan, Nozomu Hamada, Yasue Mitsukura

研究成果: Conference contribution

1 被引用数 (Scopus)

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
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 42016 3 6

Other

Other12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016
CountryMalaysia
CityMelaka
Period16/3/416/3/6

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Control and Optimization

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