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- Publisher Website: 10.1109/TII.2019.2913853
- Scopus: eid_2-s2.0-85075613296
- WOS: WOS:000498643600039
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Article: Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography
Title | Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography |
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Authors | |
Keywords | Digital holography Deep learning Superresolution Computational imaging |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 |
Citation | IEEE Transactions on Industrial Informatics, 2019, v. 15 n. 11, p. 6179-6186 How to Cite? |
Abstract | Digital holographic imaging is a powerful technique that can provide wavefront information of a three-dimensional object for biological and industrial applications. However, due to the constraint and cost of imaging sensors, the acquired digital hologram is limited in terms of pixel count, thus affecting the resolution in holographic reconstruction. To overcome this constraint, we propose a deep learning-based method to super- resolve holograms and to improve the quality of low-resolution holograms by training a convolutional neural network with large- scale data for resolution enhancement. Moreover, this algorithm can be broadly adapted to enhance the space-bandwidth product of a holographic imaging system without the need of any advanced hardware. We experimentally validate its capability using a lens-free off-axis holographic system, and compare the performance of various loss functions and interpolation methods in training such a network. |
Persistent Identifier | http://hdl.handle.net/10722/276327 |
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 4.420 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ren, Z | - |
dc.contributor.author | So, HKH | - |
dc.contributor.author | Lam, EY | - |
dc.date.accessioned | 2019-09-10T03:00:48Z | - |
dc.date.available | 2019-09-10T03:00:48Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Industrial Informatics, 2019, v. 15 n. 11, p. 6179-6186 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276327 | - |
dc.description.abstract | Digital holographic imaging is a powerful technique that can provide wavefront information of a three-dimensional object for biological and industrial applications. However, due to the constraint and cost of imaging sensors, the acquired digital hologram is limited in terms of pixel count, thus affecting the resolution in holographic reconstruction. To overcome this constraint, we propose a deep learning-based method to super- resolve holograms and to improve the quality of low-resolution holograms by training a convolutional neural network with large- scale data for resolution enhancement. Moreover, this algorithm can be broadly adapted to enhance the space-bandwidth product of a holographic imaging system without the need of any advanced hardware. We experimentally validate its capability using a lens-free off-axis holographic system, and compare the performance of various loss functions and interpolation methods in training such a network. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 | - |
dc.relation.ispartof | IEEE Transactions on Industrial Informatics | - |
dc.subject | Digital holography | - |
dc.subject | Deep learning | - |
dc.subject | Superresolution | - |
dc.subject | Computational imaging | - |
dc.title | Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography | - |
dc.type | Article | - |
dc.identifier.email | So, HKH: hso@eee.hku.hk | - |
dc.identifier.email | Lam, EY: elam@eee.hku.hk | - |
dc.identifier.authority | So, HKH=rp00169 | - |
dc.identifier.authority | Lam, EY=rp00131 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TII.2019.2913853 | - |
dc.identifier.scopus | eid_2-s2.0-85075613296 | - |
dc.identifier.hkuros | 304138 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 6179 | - |
dc.identifier.epage | 6186 | - |
dc.identifier.isi | WOS:000498643600039 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1551-3203 | - |