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A learning-based view extrapolation method for axial super-resolution
2021 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 455, p. 229-241Article in journal (Refereed) Published
Abstract [en]

Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. High refocusing precision can be essential for some light field applications like microscopy. In this paper, we propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images (EPIs). As extended numerical aperture (NA) in classical imaging, the extrapolated light field gives re-focused images with a shallower depth of field (DOF), leading to more accurate refocusing results. Most importantly, the proposed approach does not need accurate depth estimation. Experimental results with both synthetic and real light fields show that the method not only works well for light fields with small baselines as those captured by plenoptic cameras (especially for the plenoptic 1.0 cameras), but also applies to light fields with larger baselines. © 2021 Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier B.V. , 2021. Vol. 455, p. 229-241
Keywords [en]
Axial resolution, Convolutional network, Light field, Refocus precision, View extrapolation, Cameras, Learning systems, Axial direction, Axial resolutions, Convolutional networks, Extrapolation methods, Field resolution, Light fields, Minimums distance, Super resolution, Extrapolation, Article, computer model, controlled study, deep neural network, depth of field, Fourier transform, image analysis, image enhancement, learning, light related phenomena, measurement precision, optical resolution, quantitative study
Identifiers
URN: urn:nbn:se:miun:diva-43446DOI: 10.1016/j.neucom.2021.05.056Scopus ID: 2-s2.0-85107660980Archive number: 000672811100002OAI: oai:DiVA.org:miun-43446DiVA, id: diva2:1604029
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Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2021-10-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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