Deblurring sequential ocular images from multi-spectral imaging (MSI) via mutual information.
Multi-spectral imaging (MSI) produces a sequence of spectral images to capture the inner structure of different species, which was recently introduced into ocular disease diagnosis. However, the quality of MSI images can be significantly degraded by motion blur caused by the inevitable saccades and...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 6; pp. 1107 - 1114 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2018
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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=129738989&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129738989 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2018 vid: 56 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129738989 129738989 NLM29178064 10.1007/s11517-017-1743-6 NLM29178064 129738989 ppf: 1107 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deblurring sequential ocular images from multi-spectral imaging (MSI) via mutual information. aug: au: Lian, Jian Zheng, Yuanjie Jiao, Wanzhen Yan, Fang Zhao, Bojun affil: School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China sug: subj: Image Processing, Computer Assisted Methods Diagnostic Imaging Methods Algorithms Retina Ferrans and Powers Quality of Life Index Scales ab: Multi-spectral imaging (MSI) produces a sequence of spectral images to capture the inner structure of different species, which was recently introduced into ocular disease diagnosis. However, the quality of MSI images can be significantly degraded by motion blur caused by the inevitable saccades and exposure time required for maintaining a sufficiently high signal-to-noise ratio. This degradation may confuse an ophthalmologist, reduce the examination quality, or defeat various image analysis algorithms. We propose an early work specially on deblurring sequential MSI images, which is distinguished from many of the current image deblurring techniques by resolving the blur kernel simultaneously for all the images in an MSI sequence. It is accomplished by incorporating several a priori constraints including the sharpness of the latent clear image, the spatial and temporal smoothness of the blur kernel and the similarity between temporally-neighboring images in MSI sequence. Specifically, we model the similarity between MSI images with mutual information considering the different wavelengths used for capturing different images in MSI sequence. The optimization of the proposed approach is based on a multi-scale framework and stepwise optimization strategy. Experimental results from 22 MSI sequences validate that our approach outperforms several state-of-the-art techniques in natural image deblurring. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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