Blind image restoration method by PCA-based subspace generation

Brian Sumali, 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 image restoration. Original idea underlying PCA approach has two different roots. One is from the fact that PCA is relevant to variance of pixel intensity by which the missing high frequency components in blurred image should be recovered. The other comes from the idea of source separation based on PCA. In the light of PCA approach we have proposed an image restoration algorithm which contains the following three novel aspects: iterative application of PCA, Gaussian smoothing filtering for image ensemble creation, and no-reference image quality index for iteration number management. This paper aims to investigate and propose a non-iterative PCA-based image restoration with some generalizations. First, through conducted experiments the variance of Gaussian filters as well as the number of created images by them are appropriately determined. Second, weights are introduced to the principal component images. Finally, optimal weights are determined by maximizing the image quality index with no reference. Experimental results by the proposed method provide higher PSNR than the previous iterative PCA approach.

本文言語English
ホスト出版物のタイトル2015 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ204-209
ページ数6
ISBN(印刷版)9781467364997
DOI
出版ステータスPublished - 2016 3月 11
イベントInternational Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015 - Nusa Dua, Bali, Indonesia
継続期間: 2015 11月 92015 11月 12

Other

OtherInternational Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015
国/地域Indonesia
CityNusa Dua, Bali
Period15/11/915/11/12

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 信号処理

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