Abstract
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.
Original language | English |
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Title of host publication | 2015 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 204-209 |
Number of pages | 6 |
ISBN (Print) | 9781467364997 |
DOIs | |
Publication status | Published - 2016 Mar 11 |
Event | International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015 - Nusa Dua, Bali, Indonesia Duration: 2015 Nov 9 → 2015 Nov 12 |
Other
Other | International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015 |
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Country/Territory | Indonesia |
City | Nusa Dua, Bali |
Period | 15/11/9 → 15/11/12 |
Keywords
- Blind image restoration
- Gaussian blur
- Image quality assessment
- Principal component analysis
- Single image restoration
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Signal Processing