Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment.
Superresolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. The rate-limiting step in observing a superresolution image in SIM is often the reconstruction speed of the algorithm use...
| Publicado en: | International Journal of Biomedical Imaging Vol. 2024; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | computer program pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/28/2024
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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=175940326&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175940326 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16874188 1WZI jtl: International Journal of Biomedical Imaging issn: 16874188 maglogo: N pubinfo: dt: 2/28/2024 vid: 2024 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 175940326 175940326 175940326 10.1155/2024/8862387 175940326 ppf: 1 ppct: 15 formats: tig: atl: Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment. aug: au: Oh, Kwangsung Bianco, Piero R. affil: Department of Computer Science, College of Information Science & Technology, University of Nebraska Omaha, Omaha, NE 68182, USA sug: subj: Image Enhancement Microscopy Methods Graphical User Interface Algorithms Human Graphics Workflow Cost Effectiveness Analysis Computer Hardware Funding Source ab: Superresolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. The rate-limiting step in observing a superresolution image in SIM is often the reconstruction speed of the algorithm used to form a single image from as many as nine raw images. Reconstruction algorithms impose a significant computing burden due to an intricate workflow and a large number of often complex calculations to produce the final image. Further adding to the computing burden is that the code, even within the MATLAB environment, can be inefficiently written by microscopists who are noncomputer science researchers. In addition, they do not take into consideration the processing power of the graphics processing unit (GPU) of the computer. To address these issues, we present simple but efficient approaches to first revise MATLAB code, followed by conversion to GPU-optimized code. When combined with cost-effective, high-performance GPU-enabled computers, a 4- to 500-fold improvement in algorithm execution speed is observed as shown for the image denoising Hessian-SIM algorithm. Importantly, the improved algorithm produces images identical in quality to the original. pubtype: Academic Journal doctype: computer program pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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