Single image superresolution by multiple geometrical regressors

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Ninth Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2017)
PublisherIEEE
Pages1152-1155
ISBN (Electronic)978-1-5386-1542-3
Publication statusPublished - Dec 2017

Conference

Title9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PlaceMalaysia
CityKuala Lumpur
Period12 - 15 December 2017

Abstract

In this paper, to improve the quality and enhance the edge sharpness of the reconstructed image, a novel example-based single image superresolution approach is proposed, where the mappings between a low-resolution (LR) image and the corresponding high-resolution (HR) image are established based on multiple regressors. At first, multiple pairs of LR and HR geometrical dictionaries are learned from the pre-classified example patches, respectively. Then, for each atom in the geometrical dictionary, the local regressor is built up by accumulating a certain number of the most similar patches in both LR and HR spaces. In the reconstruction process, for each input LR patch, the most similar atom in each dictionary is searched and the corresponding regressor is chosen. Thus, these multiple geometrical regressors are used to get the regression coefficients in the LR space, and its HR patch can be estimated by applying the same coefficients to the corresponding multiple HR regressors. Experimental results on benchmark dataset demonstrate that our proposed method could achieve competitive results both numerically and visually compared with some state-of-the-art methods.

Research Area(s)

  • SPARSE REPRESENTATION, COMPLEXITY REDUCTION, RECONSTRUCTION, DICTIONARIES

Citation Format(s)

Single image superresolution by multiple geometrical regressors. / Zhou, Yu; Kwong, Sam; Hou, Junhui.

Proceedings - Ninth Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2017). IEEE, 2017. p. 1152-1155.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review