A Novel Algorithm for Hyperspectral Image Denoising in Medical Application.
The one of the preprocessing step for hyperspectral imagery is noise reduction. The images are received by the detector and this can be degraded by several factors like atmospherical things and device noises which emit temperature noise, processing noise and explosion noise. There are several strate...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138200099&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200099 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200099 138200099 138200099 10.1007/s10916-019-1403-5 138200099 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Algorithm for Hyperspectral Image Denoising in Medical Application. aug: au: Nageswaran, Kirubanandasarathy Nagarajan, Karthikeyan Bandiya, Ramasubramanian affil: Department of Electronics & Communication Engineering, Syed Ammal Engineering College, 623 502, Ramanathapuram, India sug: subj: Algorithms Image Processing, Computer Assisted Methods Signal Processing, Computer Assisted Methods Diagnostic Imaging Noise Prevention and Control Conceptual Framework Hypersensitivity Diagnosis Skin Diseases Diagnosis Diabetes Mellitus Diagnosis Retinal Diseases Diagnosis Multiple Linear Regression Human Descriptive Statistics ab: The one of the preprocessing step for hyperspectral imagery is noise reduction. The images are received by the detector and this can be degraded by several factors like atmospherical things and device noises which emit temperature noise, processing noise and explosion noise. There are several strategies are developed already to cut back the signal to noise magnitude relation of the hyperspectral image. However, the stationary noise of the many denoising ways developed cannot be applied on to the gauge boson noise. Thus, the each gauge boson and thermal noise square measure gift within the captured hyperspectral image (HSI). during this paper, we tend to projected a replacement denoising framework known as tensor-based filtering employing a PARAFAC tensor decomposition methodology for scale back each noise. The proposed technique is performs higher in removing noise as compared with different strategies like Multiple linear regression (MLR) algorithm and combined algorithm called multidimensional wavelet transforms with multiway wiener filter (MWPT-MWF) technique. The performance analysis of the new denoising framework has more efficient for reducing signal dependent (PN) and signal independent noise (TN) as compared with other conventional method. Hence this novel denoising approach would be more beneficial for detection of skin allergy and also this algorithm will be very useful for detection of retinal exudates and diagnosis of diabetes mellitus and retinopathy disease in medical application. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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