Image Superresolution Reconstruction via Granular Computing Clustering

被引：18

作者：

Liu, Hongbing ^{[1
]}

Zhang, Fan ^{[1
]}

Wu, Chang-an ^{[1
]}

Huang, Jun ^{[1
]}

机构：

[1] Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | 2014年 / 2014卷

关键词：

RESOLUTION;

D O I：

10.1155/2014/219636

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：10

共 20 条

[1] Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images [J].

Bahy, Ramy M. ;

Salama, Gouda I. ;

Mahmoud, Tarek A. .

SIGNAL PROCESSING, 2014, 103 :155-167

[2] Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction [J].

Benaichouche, A. N. ;

Oulhadj, H. ;

Siarry, P. .

DIGITAL SIGNAL PROCESSING, 2013, 23 (05) :1390-1400

[3]

Birkhoff G., 1967, Amer. Math. Soc. Colloq. Publ., V25

[4] On the voxel size and magnetic field strength dependence of spectral resolution in magnetic resonance spectroscopy [J].

Fleysher, Roman ;

Fleysher, Lazar ;

Liu, Songtao ;

Gonen, Oded .

MAGNETIC RESONANCE IMAGING, 2009, 27 (02) :222-232

[5]

Kaburlasos V.G., 2006, STUD COMP INTELL, V27

[6]

Kaburlasos V.G., 2014, J ENG SCI TECHNOL RE, V7, P1, DOI DOI 10.25103/ijbesar.071.01

[7] Fuzzy Inference System (FIS) Extensions Based on the Lattice Theory [J].

Kaburlasos, Vassilis G. ;

Kehagias, Athanasios .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2014, 22 (03) :531-546

[8] Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition [J].

Kaburlasos, Vassilis G. ;

Papadakis, Stelios E. ;

Papakostas, George A. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2013, 24 (10) :1526-1538

[9] Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning [J].

Kaburlasos, Vassilis G. ;

Moussiades, Lefteris ;

Vakali, Athena .

NEUROCOMPUTING, 2009, 72 (10-12) :2121-2133

[10] A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR) [J].

Kaburlasos, Vassilis G. ;

Papadakis, S. E. .

NEUROCOMPUTING, 2009, 72 (10-12) :2067-2078

← 1 2 →