{"ID":2835194,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00488","arxiv_id":"2512.00488","title":"Large-field-of-view lensless imaging with miniaturized sensors","abstract":"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 .","short_abstract":"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)...","url_abs":"https://arxiv.org/abs/2512.00488","url_pdf":"https://arxiv.org/pdf/2512.00488v1","authors":"[\"Yu Ren\",\"Xiaoling Zhang\",\"Xu Zhan\",\"Xiangdong Ma\",\"Yunqi Wang\",\"Edmund Y. Lam\",\"Tianjiao Zeng\"]","published":"2025-11-29T13:40:32Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[]","has_code":false,"code_links":[{"ID":606493,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2835194,"paper_url":"https://arxiv.org/abs/2512.00488","paper_title":"Large-field-of-view lensless imaging with miniaturized sensors","repo_url":"https://github.com/KB504-public/l2g_lensless_imaging","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}