Paper Info
Title
Image superresolution reconstruction via granular computing clustering
Abstract
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 formthe 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.
Year
DOI
Venue
2014
10.1155/2014/219636
Comp. Int. and Neurosc.
Field
DocType
Volume
Computer science,Lasso (statistics),Hypersphere,Artificial intelligence,Cluster analysis,Pattern recognition,Bicubic interpolation,Fuzzy logic,Sparse approximation,Algorithm,Granular computing,Root mean square,Machine learning
Journal
2014,
Issue
ISSN
Citations 
Issue-in-Progress
1687-5265
5
PageRank 
References 
Authors
0.48
12
4
Name
Order
Citations
PageRank
Hongbing Liu1598.74
Fan Zhang262.20
Chang-an Wu350.48
Jun Huang450.48