Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array
Nematic liquid‐crystal microlens arrays (LC‐MLAs) often exhibit chromatic aberration and low resolution, severely compromising their optical imaging quality. This study proposes an achromatic and resolution enhancement light field (ARELF) deep neural network (DNN) to address these issues. The traini...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2023-11-01
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| Series: | Advanced Photonics Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/adpr.202300154 |
| _version_ | 1797631094892265472 |
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| author | Hui Li Tian Li Si Chen Yuntao Wu |
| author_facet | Hui Li Tian Li Si Chen Yuntao Wu |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Nematic liquid‐crystal microlens arrays (LC‐MLAs) often exhibit chromatic aberration and low resolution, severely compromising their optical imaging quality. This study proposes an achromatic and resolution enhancement light field (ARELF) deep neural network (DNN) to address these issues. The training set is constructed by incorporating LC‐MLA characteristics’ degradation, retrofitting the vimeo90k dataset. The network's hidden layer is trained to learn about chromatic aberration and low resolution of LC‐MLA while extracting imaging features and fusing the information of complementary features of a light field under varying voltages. The loss function includes both chromatic aberration and overall resolution. The light field images of ZnO LC‐MLA under seven consecutive voltages are used as input to test the proposed DNN model. After experimental verification, the proposed model effectively eliminates chromatic aberration while enhancing the spatial and temporal resolution of LC‐MLA. This novel network can be utilized to optimize the design process of LC‐MLA and significantly improve its imaging performance. |
| first_indexed | 2024-03-11T11:17:04Z |
| format | Article |
| id | doaj.art-ffb29c100183453cadf65b94cc6a5352 |
| institution | Directory Open Access Journal |
| issn | 2699-9293 |
| language | English |
| last_indexed | 2024-03-11T11:17:04Z |
| publishDate | 2023-11-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Photonics Research |
| spelling | doaj.art-ffb29c100183453cadf65b94cc6a53522023-11-11T03:09:02ZengWiley-VCHAdvanced Photonics Research2699-92932023-11-01411n/an/a10.1002/adpr.202300154Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens ArrayHui Li0Tian Li1Si Chen2Yuntao Wu3School of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 ChinaSchool of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 ChinaSchool of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 ChinaSchool of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 ChinaNematic liquid‐crystal microlens arrays (LC‐MLAs) often exhibit chromatic aberration and low resolution, severely compromising their optical imaging quality. This study proposes an achromatic and resolution enhancement light field (ARELF) deep neural network (DNN) to address these issues. The training set is constructed by incorporating LC‐MLA characteristics’ degradation, retrofitting the vimeo90k dataset. The network's hidden layer is trained to learn about chromatic aberration and low resolution of LC‐MLA while extracting imaging features and fusing the information of complementary features of a light field under varying voltages. The loss function includes both chromatic aberration and overall resolution. The light field images of ZnO LC‐MLA under seven consecutive voltages are used as input to test the proposed DNN model. After experimental verification, the proposed model effectively eliminates chromatic aberration while enhancing the spatial and temporal resolution of LC‐MLA. This novel network can be utilized to optimize the design process of LC‐MLA and significantly improve its imaging performance.https://doi.org/10.1002/adpr.202300154achromaticdeep neural networkslight fieldsliquid‐crystal microlens arraysresolution enhancement |
| spellingShingle | Hui Li Tian Li Si Chen Yuntao Wu Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array Advanced Photonics Research achromatic deep neural networks light fields liquid‐crystal microlens arrays resolution enhancement |
| title | Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array |
| title_full | Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array |
| title_fullStr | Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array |
| title_full_unstemmed | Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array |
| title_short | Achromatic and Resolution Enhancement Light Field Deep Neural Network for ZnO Nematic Liquid Crystal Microlens Array |
| title_sort | achromatic and resolution enhancement light field deep neural network for zno nematic liquid crystal microlens array |
| topic | achromatic deep neural networks light fields liquid‐crystal microlens arrays resolution enhancement |
| url | https://doi.org/10.1002/adpr.202300154 |
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