{"ID":2827332,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16065","arxiv_id":"2512.16065","title":"Single-View Tomographic Reconstruction Using Learned Primal Dual","abstract":"The Learned Primal Dual (LPD) method has shown promising results in various tomographic reconstruction modalities, particularly under challenging acquisition restrictions such as limited viewing angles or a limited number of views. We investigate the performance of LPD in a more extreme case: single-view tomographic reconstructions of axially-symmetric targets. This study considers two modalities: the first assumes low-divergence or parallel X-rays. The second models a cone-beam X-ray imaging testbed. For both modalities, training data is generated using closed-form integral transforms, or physics-based ray-tracing software, then corrupted with blur and noise. Our results are then compared against common numerical inversion methodologies.","short_abstract":"The Learned Primal Dual (LPD) method has shown promising results in various tomographic reconstruction modalities, particularly under challenging acquisition restrictions such as limited viewing angles or a limited number of views. We investigate the performance of LPD in a more extreme case: single-view tomographic re...","url_abs":"https://arxiv.org/abs/2512.16065","url_pdf":"https://arxiv.org/pdf/2512.16065v2","authors":"[\"Sean Breckling\",\"Matthew Swan\",\"Keith D. Tan\",\"Derek Wingard\",\"Brandon Baldonado\",\"Yoohwan Kim\",\"Ju-Yeon Jo\",\"Evan Scott\",\"Jordan Pillow\"]","published":"2025-12-18T01:19:30Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[]","has_code":false}