Image Superresolution Reconstruction via Granular Computing Clustering
Joint Authors
Liu, Hongbing
Zhang, Fan
Huang, Jun
Wu, Chang-an
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper.
Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches.
Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules.
Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso.
Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.
American Psychological Association (APA)
Liu, Hongbing& Zhang, Fan& Wu, Chang-an& Huang, Jun. 2014. Image Superresolution Reconstruction via Granular Computing Clustering. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034643
Modern Language Association (MLA)
Liu, Hongbing…[et al.]. Image Superresolution Reconstruction via Granular Computing Clustering. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034643
American Medical Association (AMA)
Liu, Hongbing& Zhang, Fan& Wu, Chang-an& Huang, Jun. Image Superresolution Reconstruction via Granular Computing Clustering. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034643
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034643