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Opt."],"published-print":{"date-parts":[[2020,7,10]]},"abstract":"<jats:p>An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial (<jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>x<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>, <jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>y<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>) and spectral (<jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>\u03bb<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>) information of incoming light. The IMS maps a three-dimensional (3D) datacube (<jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>x<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>, <jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>y<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>, <jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>\u03bb<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>) to a two-dimensional (2D) detector array (<jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>x<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>, <jats:inline-formula>\n      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n\t<mml:mi>y<\/mml:mi>\n      <\/mml:math>\n    <\/jats:inline-formula>) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.<\/jats:p>","DOI":"10.1364\/ao.395988","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T14:30:16Z","timestamp":1591713016000},"page":"6062","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Development of a fast calibration method for image mapping spectrometry"],"prefix":"10.1364","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5912-3625","authenticated-orcid":true,"given":"Qi","family":"Cui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongchan","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9126-9491","authenticated-orcid":true,"given":"Rishyashring R.","family":"Iyer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8118-9188","authenticated-orcid":true,"given":"Mantas","family":"\u017durauskas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9386-5630","authenticated-orcid":true,"given":"Stephen A.","family":"Boppart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. 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