Roles of Deep Learning in Optical Imaging
Imaging-based problem-solving approaches are an exemplary way of handling problems in various scientific applications. With an increased demand for automation, artificial intelligence techniques have shown exponential growth in recent years. In this context, deep-learning-based “learned” solutions h...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/34/1/6 |
| _version_ | 1797580215697801216 |
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| author | Vineela Chandra Dodda Inbarasan Muniraj |
| author_facet | Vineela Chandra Dodda Inbarasan Muniraj |
| author_sort | Vineela Chandra Dodda |
| collection | DOAJ |
| description | Imaging-based problem-solving approaches are an exemplary way of handling problems in various scientific applications. With an increased demand for automation, artificial intelligence techniques have shown exponential growth in recent years. In this context, deep-learning-based “learned” solutions have been widely adopted in many applications and are thus slowly becoming an inevitable alternative tool. It is known that in contrast to the conventional “physics-based” approach, deep learning models are a “data-driven” approach, where the outcomes are based on data analysis and interpretation. Thus, deep learning approaches have been applied in several (optical and computational) imaging-based scientific problems such as denoising, phase retrieval, hologram reconstruction, and histopathology, to name a few. In this work, we present two deep-learning networks for 3D image denoising and off-focus voxel removal. |
| first_indexed | 2024-03-10T22:48:02Z |
| format | Article |
| id | doaj.art-26584bcfab014ca1996fcc370234a15b |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-10T22:48:02Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-26584bcfab014ca1996fcc370234a15b2023-11-19T10:33:08ZengMDPI AGEngineering Proceedings2673-45912023-03-01341610.3390/HMAM2-14123Roles of Deep Learning in Optical ImagingVineela Chandra Dodda0Inbarasan Muniraj1Department of Electronics and Communication Engineering, School of Engineering and Applied Sciences, SRM University AP, Amaravathi 522240, IndiaDepartment of Electronics and Communication Engineering, School of Engineering and Applied Sciences, SRM University AP, Amaravathi 522240, IndiaImaging-based problem-solving approaches are an exemplary way of handling problems in various scientific applications. With an increased demand for automation, artificial intelligence techniques have shown exponential growth in recent years. In this context, deep-learning-based “learned” solutions have been widely adopted in many applications and are thus slowly becoming an inevitable alternative tool. It is known that in contrast to the conventional “physics-based” approach, deep learning models are a “data-driven” approach, where the outcomes are based on data analysis and interpretation. Thus, deep learning approaches have been applied in several (optical and computational) imaging-based scientific problems such as denoising, phase retrieval, hologram reconstruction, and histopathology, to name a few. In this work, we present two deep-learning networks for 3D image denoising and off-focus voxel removal.https://www.mdpi.com/2673-4591/34/1/6optical 3D imagingunsupervised denoisingoff-focus removalintegral imaging |
| spellingShingle | Vineela Chandra Dodda Inbarasan Muniraj Roles of Deep Learning in Optical Imaging Engineering Proceedings optical 3D imaging unsupervised denoising off-focus removal integral imaging |
| title | Roles of Deep Learning in Optical Imaging |
| title_full | Roles of Deep Learning in Optical Imaging |
| title_fullStr | Roles of Deep Learning in Optical Imaging |
| title_full_unstemmed | Roles of Deep Learning in Optical Imaging |
| title_short | Roles of Deep Learning in Optical Imaging |
| title_sort | roles of deep learning in optical imaging |
| topic | optical 3D imaging unsupervised denoising off-focus removal integral imaging |
| url | https://www.mdpi.com/2673-4591/34/1/6 |
| work_keys_str_mv | AT vineelachandradodda rolesofdeeplearninginopticalimaging AT inbarasanmuniraj rolesofdeeplearninginopticalimaging |