Large-field-of-view lensless imaging with miniaturized sensors
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908681718726656 |
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| author | Ren, Yu Zhang, Xiaoling Zhan, Xu Ma, Xiangdong Wang, Yunqi Lam, Edmund Y. Zeng, Tianjiao |
| author_facet | Ren, Yu Zhang, Xiaoling Zhan, Xu Ma, Xiangdong Wang, Yunqi Lam, Edmund Y. Zeng, Tianjiao |
| contents | Lensless cameras replace bulky optics with thin modulation masks, enabling compact imaging systems. However, existing methods rely on an idealized model that assumes a globally shift-invariant point spread function (PSF) and sufficiently large sensors. In reality, the PSF varies spatially across the field of view (FOV), and finite sensor boundaries truncate modulated light--effects that intensify as sensors shrink, degrading peripheral reconstruction quality and limiting the effective FOV. We address these limitations through a local-to-global hierarchical framework grounded in a locally shift-invariant convolution model that explicitly accounts for PSF variation and sensor truncation. Patch-wise learned deconvolution first adaptively estimates local PSFs and reconstructs regions independently. A hierarchical enhancement network then progressively expands its receptive field--from small patches through intermediate blocks to the full image--integrating fine local details with global contextual information. Experiments on public datasets show that our method achieves superior reconstruction quality over a larger effective FOV with significantly reduced sensor sizes. Under extreme miniaturization--sensors reduced to 8% of the original area--we achieve improvements of 2 dB (PSNR) and 5% (SSIM), with particularly notable gains in structural fidelity. Code is available at https://github.com/KB504-public/l2g_lensless_imaging . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-field-of-view lensless imaging with miniaturized sensors Ren, Yu Zhang, Xiaoling Zhan, Xu Ma, Xiangdong Wang, Yunqi Lam, Edmund Y. Zeng, Tianjiao Image and Video Processing Lensless cameras replace bulky optics with thin modulation masks, enabling compact imaging systems. However, existing methods rely on an idealized model that assumes a globally shift-invariant point spread function (PSF) and sufficiently large sensors. In reality, the PSF varies spatially across the field of view (FOV), and finite sensor boundaries truncate modulated light--effects that intensify as sensors shrink, degrading peripheral reconstruction quality and limiting the effective FOV. We address these limitations through a local-to-global hierarchical framework grounded in a locally shift-invariant convolution model that explicitly accounts for PSF variation and sensor truncation. Patch-wise learned deconvolution first adaptively estimates local PSFs and reconstructs regions independently. A hierarchical enhancement network then progressively expands its receptive field--from small patches through intermediate blocks to the full image--integrating fine local details with global contextual information. Experiments on public datasets show that our method achieves superior reconstruction quality over a larger effective FOV with significantly reduced sensor sizes. Under extreme miniaturization--sensors reduced to 8% of the original area--we achieve improvements of 2 dB (PSNR) and 5% (SSIM), with particularly notable gains in structural fidelity. Code is available at https://github.com/KB504-public/l2g_lensless_imaging . |
| title | Large-field-of-view lensless imaging with miniaturized sensors |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2512.00488 |