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Image superresolution reconstruction via granular computing clustering.

Liu H, Zhang F, Wu CA, Huang J - Comput Intell Neurosci (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

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.

Show MeSH
The cross-points between hypersphere granules and the line through C12. P1 and P2 are the cross-points between hypersphere G1 = (C1, R1) and the line through C1 and C2, and Q1 and Q2 are the cross-points between hypersphere G2 = (C2, R2) and the line through C1 and C2.
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Related In: Results  -  Collection


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fig1: The cross-points between hypersphere granules and the line through C12. P1 and P2 are the cross-points between hypersphere G1 = (C1, R1) and the line through C1 and C2, and Q1 and Q2 are the cross-points between hypersphere G2 = (C2, R2) and the line through C1 and C2.

Mentions: Secondly, the cross-points between the hypersphere G1 and the line through C12 are P1 = C1 − C12R1 and P2 = C1 + C12R1. The cross-points between the hypersphere G2 and the line through C12 are Q1 = C2 − R2C21 and Q2 = C2 + R2C21. The cross-points are shown in Figure 1.


Image superresolution reconstruction via granular computing clustering.

Liu H, Zhang F, Wu CA, Huang J - Comput Intell Neurosci (2014)

The cross-points between hypersphere granules and the line through C12. P1 and P2 are the cross-points between hypersphere G1 = (C1, R1) and the line through C1 and C2, and Q1 and Q2 are the cross-points between hypersphere G2 = (C2, R2) and the line through C1 and C2.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4291197&req=5

fig1: The cross-points between hypersphere granules and the line through C12. P1 and P2 are the cross-points between hypersphere G1 = (C1, R1) and the line through C1 and C2, and Q1 and Q2 are the cross-points between hypersphere G2 = (C2, R2) and the line through C1 and C2.
Mentions: Secondly, the cross-points between the hypersphere G1 and the line through C12 are P1 = C1 − C12R1 and P2 = C1 + C12R1. The cross-points between the hypersphere G2 and the line through C12 are Q1 = C2 − R2C21 and Q2 = C2 + R2C21. The cross-points are shown in Figure 1.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

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.

Show MeSH