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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2509.12454 |
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| _version_ | 1866911321422823424 |
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| author | Goswami, Neha Anastasio, Mark A. |
| author_facet | Goswami, Neha Anastasio, Mark A. |
| contents | The space-bandwidth product (SBP) imposes a fundamental limitation in achieving high-resolution and large field-of-view image acquisitions simultaneously. High-NA objectives provide fine structural detail at the cost of reduced spatial coverage and slower scanning as compared to a low-NA objective, while low-NA objectives offer wide fields of view but compromised resolution. Here, we introduce LensPlus, a deep learning-based framework that enhances the SBP of quantitative phase imaging (QPI) without requiring hardware modifications. By training on paired datasets acquired with low-NA and high-NA objectives, LensPlus learns to recover high-frequency features lost in low-NA measurements, effectively bridging the resolution gap while preserving the large field of view thereby increasing the SBP. We demonstrate that LensPlus can transform images acquired with a 10x/0.3 NA objective (40x/0.95 NA for another model) to a quality comparable to that obtained using a 40x/0.95 NA objective (100x/1.45NA for the second model), resulting in a 2D-SBP improvement of approximately 3.5x (2.04x for the second model). Importantly, unlike adversarial models, LensPlus employs non-generative model to minimize image hallucinations and ensure quantitative fidelity as verified through spectral analysis. Beyond QPI, LensPlus is broadly applicable to other lens-based imaging modalities, enabling wide-field, high-resolution imaging for time-lapse studies, large-area tissue mapping, and applications where high-NA oil objectives are impractical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LensPlus: A High Space-bandwidth Optical Imaging Technique Goswami, Neha Anastasio, Mark A. Optics The space-bandwidth product (SBP) imposes a fundamental limitation in achieving high-resolution and large field-of-view image acquisitions simultaneously. High-NA objectives provide fine structural detail at the cost of reduced spatial coverage and slower scanning as compared to a low-NA objective, while low-NA objectives offer wide fields of view but compromised resolution. Here, we introduce LensPlus, a deep learning-based framework that enhances the SBP of quantitative phase imaging (QPI) without requiring hardware modifications. By training on paired datasets acquired with low-NA and high-NA objectives, LensPlus learns to recover high-frequency features lost in low-NA measurements, effectively bridging the resolution gap while preserving the large field of view thereby increasing the SBP. We demonstrate that LensPlus can transform images acquired with a 10x/0.3 NA objective (40x/0.95 NA for another model) to a quality comparable to that obtained using a 40x/0.95 NA objective (100x/1.45NA for the second model), resulting in a 2D-SBP improvement of approximately 3.5x (2.04x for the second model). Importantly, unlike adversarial models, LensPlus employs non-generative model to minimize image hallucinations and ensure quantitative fidelity as verified through spectral analysis. Beyond QPI, LensPlus is broadly applicable to other lens-based imaging modalities, enabling wide-field, high-resolution imaging for time-lapse studies, large-area tissue mapping, and applications where high-NA oil objectives are impractical. |
| title | LensPlus: A High Space-bandwidth Optical Imaging Technique |
| topic | Optics |
| url | https://arxiv.org/abs/2509.12454 |