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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.

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The average female face image magnified by a factor of 3. (a) Low-resolution image, (b) the original image, (c) superresolution image by bicubic interpolation, (d) superresolution image by sparse representation, (e) superresolution image by NNLasso, and (f) superresolution image by GrC.
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fig10: The average female face image magnified by a factor of 3. (a) Low-resolution image, (b) the original image, (c) superresolution image by bicubic interpolation, (d) superresolution image by sparse representation, (e) superresolution image by NNLasso, and (f) superresolution image by GrC.

Mentions: We compared GrC clustering with sparse representation, bicubic interpolation, and NNLasso, on five test images of a flower [18], girl [18], Lenna [21], average female face [22], and average male face [22]. Firstly, training set including 999910 image patches is obtained in the sampling stage, and the redundancy of training set is reduced by GrC and sparse representation. Secondly, the LS images of testing images are resized by nearest method. Thirdly, the SR images are obtained by sparse representation, bicubic interpolation, NNLasso, and GrC clustering. The root mean square error (RMSE) between the superresolution images and the original images is listed in Table 1. From the table, we can see that the superresolution images by GrC are better than the superresolution by bicubic interpolation (bicubic), sparse representation (sparse), and NNLasso. The LS images, original images, and SR images are shown in Figures 7, 8, 9, 10, and 11. For human visual, the original images are the most clear, and the reconstruction images by NNLasso are blurry.


Image superresolution reconstruction via granular computing clustering.

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

The average female face image magnified by a factor of 3. (a) Low-resolution image, (b) the original image, (c) superresolution image by bicubic interpolation, (d) superresolution image by sparse representation, (e) superresolution image by NNLasso, and (f) superresolution image by GrC.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: The average female face image magnified by a factor of 3. (a) Low-resolution image, (b) the original image, (c) superresolution image by bicubic interpolation, (d) superresolution image by sparse representation, (e) superresolution image by NNLasso, and (f) superresolution image by GrC.
Mentions: We compared GrC clustering with sparse representation, bicubic interpolation, and NNLasso, on five test images of a flower [18], girl [18], Lenna [21], average female face [22], and average male face [22]. Firstly, training set including 999910 image patches is obtained in the sampling stage, and the redundancy of training set is reduced by GrC and sparse representation. Secondly, the LS images of testing images are resized by nearest method. Thirdly, the SR images are obtained by sparse representation, bicubic interpolation, NNLasso, and GrC clustering. The root mean square error (RMSE) between the superresolution images and the original images is listed in Table 1. From the table, we can see that the superresolution images by GrC are better than the superresolution by bicubic interpolation (bicubic), sparse representation (sparse), and NNLasso. The LS images, original images, and SR images are shown in Figures 7, 8, 9, 10, and 11. For human visual, the original images are the most clear, and the reconstruction images by NNLasso are blurry.

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