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

Brian Sumali, Haslina Sarkan, Nozomu Hamada, Yasue Mitsukura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages183-187
Number of pages5
ISBN (Electronic)9781467387804
DOIs
Publication statusPublished - 2016 Jul 18
Event12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016 - Melaka, Malaysia
Duration: 2016 Mar 42016 Mar 6

Other

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

Fingerprint

Super-resolution
Image Quality
Principal component analysis
Image quality
Principal Component Analysis
Subspace
Optimization
Restoration
Atmospheric turbulence
Atmospheric Turbulence
Image Database
Gaussian White Noise
Image resolution
Experiment
Experiments

Keywords

  • Image Quality Assessment
  • Noise Robustness
  • Principal Component Analysis
  • Single Image
  • Super Resolution

ASJC Scopus subject areas

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

Cite this

Sumali, B., Sarkan, H., Hamada, N., & Mitsukura, Y. (2016). Single image Super Resolution by no-reference image quality index optimization in PCA subspace. In Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016 (pp. 183-187). [7515828] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSPA.2016.7515828

Single image Super Resolution by no-reference image quality index optimization in PCA subspace. / Sumali, Brian; Sarkan, Haslina; Hamada, Nozomu; Mitsukura, Yasue.

Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 183-187 7515828.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sumali, B, Sarkan, H, Hamada, N & Mitsukura, Y 2016, Single image Super Resolution by no-reference image quality index optimization in PCA subspace. in Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016., 7515828, Institute of Electrical and Electronics Engineers Inc., pp. 183-187, 12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016, Melaka, Malaysia, 16/3/4. https://doi.org/10.1109/CSPA.2016.7515828
Sumali B, Sarkan H, Hamada N, Mitsukura Y. Single image Super Resolution by no-reference image quality index optimization in PCA subspace. In Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 183-187. 7515828 https://doi.org/10.1109/CSPA.2016.7515828
Sumali, Brian ; Sarkan, Haslina ; Hamada, Nozomu ; Mitsukura, Yasue. / Single image Super Resolution by no-reference image quality index optimization in PCA subspace. Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 183-187
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