A novel method for enhancement of radiometric resolution using image fusion

Rao, C. V.; Rao, K. M. M.; Reddy, P. Shasidhar; Pujar, G. S.

doi:10.1016/j.jag.2008.02.007

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…(Nunez et al, 1999;Pohl, 1999;Svab & Ostir, 2006;Ling et al, 2008;Rao et al, 2008;Artyushkova et al, 2009) For example, a typical tradeoff which often occurs in remote sensing is between spatial resolution and resolution in wavelength. It may be possible and desirable to combine a low resolution color image and a high resolution monochromatic image to produce a color image with high spatial resolution.…”

Section: Example 3 Afm and Xpsmentioning

confidence: 99%

Multitechnique Fusion of Imaging Data for Heterogeneous Materials

Artyushkova¹,

Fenton²,

Farrar³

et al. 2011

Image Fusion and Its Applications

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Section: Example 3 Afm and Xpsmentioning

confidence: 99%

Multitechnique Fusion of Imaging Data for Heterogeneous Materials

Artyushkova¹,

Fenton²,

Farrar³

et al. 2011

Image Fusion and Its Applications

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“…One can approach fusion of XPS and AFM images as the problem of combining images of drastically different resolutions, captured with different instruments and with different spectral characteristics into single image with the goal of facilitating interpretation and analysis. Pohl, 1999;Svab & Ostir, 2006;Rao et al, 2008;Artyushkova et al, 2009) For example, a typical tradeoff which often occurs in remote sensing is between spatial resolution and resolution in wavelength. It may be possible and desirable to combine a low resolution color image and a high resolution monochromatic image to produce a color image with high spatial resolution.…”

Section: Example 3 Afm and Xpsmentioning

confidence: 99%

Image Fusion and Its Applications

Zheng¹

2011

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Yufeng Zheng received his Ph.D. degree in Optical Engineering/Image Processing from Tianjin University (Tianjin, China) in 1997. He is presently with Alcorn State University (Mississippi, USA) as an associate professor. Dr. Zheng serves as a program director of the Computer Network and Information Technology major, as well as the director of Pattern Recognition and Image Analysis Lab. He is the principle investigator on three federal research grants in night vision enhancement, thermal face recognition, and multispectral face recognition. He was the Co-PI on a breast cancer detection research grant. Dr. Zheng holds two patents on glaucoma classification and face recognition, and has published one book, six book chapters, and more than 70 peer-reviewed papers. His research interests include biomedical imaging, pattern recognition, biometrics, information fusion, colorization, bio-inspired image analysis, and computer-aided diagnosis. Dr. Zheng is a Cisco Certified Network Professional (CCNP), a senior member of SPIE, a member of IEEE & Computer Society, as well as a technical reviewer.

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“…Improvement of picture information for human interpretation and helping representation for automatic machine perception are two principal application areas [ 1 ]. The resolution of a digital image can be classified in many different ways: pixel resolution, spatial resolution, spectral resolution, temporal resolution, and radiometric resolution [ 2 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The basic idea behind SR is to combine the nonredundant information contained in multiple LR frames to generate a HR image. A closely related technique with SR is the single image interpolation approach, which can be also used to increase the image size [ 1 , 5 ]. However, since there is no additional information provided, the quality of the single image interpolation is very much limited due to the ill-posed nature of the problem, and the lost frequency components cannot be recovered.…”

Section: Introductionmentioning

confidence: 99%

Image Superresolution Reconstruction via Granular Computing Clustering

Liu

Zhang

et al. 2014

Computational Intelligence and Neuroscience

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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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A novel method for enhancement of radiometric resolution using image fusion

Cited by 9 publications

References 3 publications

Multitechnique Fusion of Imaging Data for Heterogeneous Materials

Multitechnique Fusion of Imaging Data for Heterogeneous Materials

Image Fusion and Its Applications

Image Superresolution Reconstruction via Granular Computing Clustering

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