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Opt."],"published-print":{"date-parts":[[2025,11,10]]},"abstract":"<jats:p>A high-flux, high-resolution Offner imaging spectrometer with a compact configuration is proposed and demonstrated. By introducing a custom-designed lens group between the entrance slit and the primary concave mirror, the effective slit is optically transformed into a virtual slit with extended divergence and path length. This design fundamentally alleviates astigmatism and enables a larger numerical aperture (NA\u00a0=\u00a00.3), while preserving the intrinsic aberration-free benefits of the Offner configuration. Theoretical modeling and optical analysis were conducted to derive aplanatic conditions and optimize focal power distribution. A three-lens assembly, including a toroidal surface, was employed to decouple meridional and sagittal focal lengths and correct residual aberrations. The optimized system covers the 400\u20131000\u00a0nm spectral range with a measured spectral resolution of 1.35\u00a0nm and maintains an MTF&gt;0.7 at 28\u00a0lp\/mm across the full field of view. Compared to a conventional Offner spectrometer of equal resolution, the proposed design improves optical throughput by a factor of 2.25 and reduces system volume by 14.5%. Laboratory calibration using a mercury\u2013argon lamp confirmed its high-resolution capabilities, and field experiments demonstrated excellent spectral and spatial imaging performance. The improved Offner spectrometer offers a compact, aberration-free, and high-resolution solution for applications requiring high-precision spectral imaging.<\/jats:p>","DOI":"10.1364\/ao.573670","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:01:06Z","timestamp":1760364066000},"page":"9691","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-flux, high-resolution compact Offner imaging spectrometer for hyperspectral applications"],"prefix":"10.1364","volume":"64","author":[{"given":"Jing","family":"Lin","sequence":"first","affiliation":[{"name":"Hefei Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Hu","sequence":"additional","affiliation":[{"name":"Hefei Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04c4dkn09","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"ao-64-32-9691-R1","doi-asserted-by":"publisher","first-page":"9156","DOI":"10.1364\/OE.14.009156","type":"journal-article","volume":"14","author":"Prieto-Blanco","year":"2006","journal-title":"Opt. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}