Single image superresolution by multiple geometrical regressors

Yu ZHOU, Sam KWONG, Junhui HOU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of APSIPA Annual Summit and Conference 2017
Number of pages4
ISBN (Electronic)9781538615423
ISBN (Print)9781538615430
Publication statusPublished - Dec 2017
Externally publishedYes
Event2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference - Kuala Lumpur, Malaysia
Duration: 12 Dec 201715 Dec 2017


Conference2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Abbreviated titleAPSIPA ASC 2017
CityKuala Lumpur

Bibliographical note

This work was supported in part by the National Science Foundation of China under Grants 61702336 and 61672443, in part by Shenzhen Emerging Industries of the Strategic Basic Research project under JCYJ20170302154254147 and in part by the Hong Kong RGC General Research Fund GRF Grant 9042038 (CityU 11205314).


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