{"ID":2843829,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06853","arxiv_id":"2511.06853","title":"Computational TIRF enables optical sectioning beyond the evanescent field for widefield fluorescence microscopy","abstract":"The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intrinsically restricted to shallow imaging depths. Here we present computational TIRF (cTIRF), a deep learning-based imaging modality that generates TIRF-like sectioned images directly from conventional widefield epifluorescence measurements without any optical modification. By integrating a physics-informed forward model into network training, cTIRF achieves effective background suppression and axial resolution enhancement while maintaining consistency with the measured widefield data. We demonstrate that cTIRF recovers near-surface structures with performance comparable to experimental TIRF, and further enables both single-frame and volumetric sectioned reconstruction in densely labeled samples where conventional TIRF fails. This work establishes cTIRF as a practical and deployable alternative to hardware-based optical sectioning in fluorescence microscopy, enabled by rapid adaptation to new imaging systems with minimal calibration data.","short_abstract":"The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intri...","url_abs":"https://arxiv.org/abs/2511.06853","url_pdf":"https://arxiv.org/pdf/2511.06853v2","authors":"[\"Qiushi Li\",\"Celi Lou\",\"Yanfang Cheng\",\"Bilang Gong\",\"Xinlin Chen\",\"Hao Chen\",\"Baowan Li\",\"Jieli Wang\",\"Yulin Wang\",\"Sipeng Yang\",\"Yunqing Tang\",\"Luru Dai\"]","published":"2025-11-10T08:52:56Z","proceeding":"physics.optics","tasks":"[\"physics.optics\",\"cs.AI\"]","methods":"[]","has_code":false}