MsRAN: a multi-scale residual attention network for multi-model image fusion.
Fusion is a critical step in image processing tasks. Recently, deep learning networks have been considerably applied in information fusion. But the significant limitation of existing image fusion methods is the inability to highlight typical regions of the source image and retain sufficient useful i...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 12; pp. 3615 - 3635 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2022
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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=160112202&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160112202 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2022 vid: 60 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160112202 160112202 NLM36261766 10.1007/s11517-022-02690-1 NLM36261766 160112202 ppf: 3615 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MsRAN: a multi-scale residual attention network for multi-model image fusion. aug: au: Wang, Jing Yu, Long Tian, Shengwei affil: College of Software Engineering, Xin Jiang University, 830000, Urumqi, China sug: subj: Algorithms Image Processing, Computer Assisted Methods Gravitation ab: Fusion is a critical step in image processing tasks. Recently, deep learning networks have been considerably applied in information fusion. But the significant limitation of existing image fusion methods is the inability to highlight typical regions of the source image and retain sufficient useful information. To address the problem, the paper proposes a multi-scale residual attention network (MsRAN) to fully exploit the image feature. Its generator network contains two information refinement networks and one information integration network. The information refinement network extracts feature at different scales using convolution kernels of different sizes. The information integration network, with a merging block and an attention block added, prevents the underutilization of information in the intermediate layers and forces the generator to focus on salient regions in multi-modal source images. Furthermore, in the phase of model training, we add an information loss function and adopt a dual adversarial structure, enabling the model to capture more details. Qualitative and quantitative experiments on publicly available datasets validate that the proposed method provides better visual results than other methods and retains more detail information. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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