Wavelet Domain Multidictionary Learning for Single Image Super-Resolution
Image super-resolution (SR) aims at recovering the high-frequency (HF) details of a high-resolution (HR) image according to the given low-resolution (LR) image and some priors about natural images. Learning the relationship of the LR image and its corresponding HF details to guide the reconstruction...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/526508 |
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| _version_ | 1832563170038775808 |
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| author | Xiaomin Wu Jiulun Fan Jian Xu Yanzi Wang |
| author_facet | Xiaomin Wu Jiulun Fan Jian Xu Yanzi Wang |
| author_sort | Xiaomin Wu |
| collection | DOAJ |
| description | Image super-resolution (SR) aims at recovering
the high-frequency (HF) details of a high-resolution
(HR) image according to the given low-resolution (LR)
image and some priors about natural images. Learning
the relationship of the LR image and its corresponding
HF details to guide the reconstruction of the HR image
is needed. In order to alleviate the uncertainty in
HF detail prediction, the HR and LR images are usually
decomposed into 4 subbands after 1-level discrete
wavelet transformation (DWT), including an approximation
subband and three detail subbands. From our
observation, we found the approximation subbands of
the HR image and the corresponding bicubic interpolated
image are very similar but the respective detail
subbands are different. Therefore, an algorithm to learn
4 coupled principal component analysis (PCA) dictionaries
to describe the relationship between the approximation
subband and the detail subbands is proposed
in this paper. Comparisons with various state-of-the-art
methods by experiments showed that our proposed
algorithm is superior to some related works. |
| format | Article |
| id | doaj-art-9c3ad8fa93f24884b836da2e2e14358a |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-9c3ad8fa93f24884b836da2e2e14358a2025-02-03T01:20:48ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/526508526508Wavelet Domain Multidictionary Learning for Single Image Super-ResolutionXiaomin Wu0Jiulun Fan1Jian Xu2Yanzi Wang3School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaImage super-resolution (SR) aims at recovering the high-frequency (HF) details of a high-resolution (HR) image according to the given low-resolution (LR) image and some priors about natural images. Learning the relationship of the LR image and its corresponding HF details to guide the reconstruction of the HR image is needed. In order to alleviate the uncertainty in HF detail prediction, the HR and LR images are usually decomposed into 4 subbands after 1-level discrete wavelet transformation (DWT), including an approximation subband and three detail subbands. From our observation, we found the approximation subbands of the HR image and the corresponding bicubic interpolated image are very similar but the respective detail subbands are different. Therefore, an algorithm to learn 4 coupled principal component analysis (PCA) dictionaries to describe the relationship between the approximation subband and the detail subbands is proposed in this paper. Comparisons with various state-of-the-art methods by experiments showed that our proposed algorithm is superior to some related works.http://dx.doi.org/10.1155/2015/526508 |
| spellingShingle | Xiaomin Wu Jiulun Fan Jian Xu Yanzi Wang Wavelet Domain Multidictionary Learning for Single Image Super-Resolution Journal of Electrical and Computer Engineering |
| title | Wavelet Domain Multidictionary Learning for Single Image Super-Resolution |
| title_full | Wavelet Domain Multidictionary Learning for Single Image Super-Resolution |
| title_fullStr | Wavelet Domain Multidictionary Learning for Single Image Super-Resolution |
| title_full_unstemmed | Wavelet Domain Multidictionary Learning for Single Image Super-Resolution |
| title_short | Wavelet Domain Multidictionary Learning for Single Image Super-Resolution |
| title_sort | wavelet domain multidictionary learning for single image super resolution |
| url | http://dx.doi.org/10.1155/2015/526508 |
| work_keys_str_mv | AT xiaominwu waveletdomainmultidictionarylearningforsingleimagesuperresolution AT jiulunfan waveletdomainmultidictionarylearningforsingleimagesuperresolution AT jianxu waveletdomainmultidictionarylearningforsingleimagesuperresolution AT yanziwang waveletdomainmultidictionarylearningforsingleimagesuperresolution |