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 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.
authors.affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
authors.forename: Hongbing
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
authors.forename: Chang-an
authors.initials: CA
authors.lastname: Wu
authors.affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003952:Diagnostic Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated