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Express"],"published-print":{"date-parts":[[2026,7,27]]},"abstract":"<jats:p>Recent advancements in wide-field mid-infrared photothermal (MIP) microscopy offer rapid chemical contrast for biological, biomedical, and polymer analysis at submicron resolution. Currently, the field of view (FOV), signal-to-noise ratio (SNR), and imaging speed are limited by mid-infrared pump fluence, non-uniform mid-infrared illumination, relatively small photothermal scattering change, and consequently the need for multiple camera frame averaging to achieve photothermal contrast. To correct non-uniform illumination, expand FOV, and improve SNR, flat-field correction, feature-based image stitching, and deep-learning image denoising are integrated within our previously developed wide-field quantum-cascade-laser MIP workflow. A feature-based stitching algorithm with flat-field correction combines individual small MIP frames into large composite mosaics, which is demonstrated on expanded MIP images of tuberculosis-infected lung tissue. Quantitatively, stitching delivered high-fidelity reconstructions, yielding structural similarity index measure (SSIM) and normalized cross-correlation (NCC) values &gt;0.97 in overlaps. For denoising, a residual dense U-Net (RDUNet) is trained with high-frame-average data to predict high-SNR images from low-frame-average inputs. The method is applied to wide-field MIP images of polyethylene terephthalate (PET) microparticles. RDUNet boosted SSIM by &gt;0.7 and peak SNR by &gt;5\u2005dB while predicting clean images of PET particles in less than 1 second. Overall, stitching of mosaics from multiple wide-field MIP images at low-frame numbers paves the way towards large-area, high-contrast MIP imaging, advancing chemical imaging towards high-throughput applications.<\/jats:p>","DOI":"10.1364\/oe.605038","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T07:00:08Z","timestamp":1781852408000},"page":"27422","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based denoising and computational field-of-view extension towards rapid wide-field mid-infrared photothermal imaging"],"prefix":"10.1364","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9080-6430","authenticated-orcid":true,"given":"Anooj Thayyil","family":"Raveendran","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02se0t636","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supatcharee","family":"Cael","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02se0t636","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4257-593X","authenticated-orcid":true,"given":"Juergen","family":"Popp","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02se0t636","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Friedrich-Schiller-University Jena, Institute of Physical Chemistry, Member of Leibniz Research Alliance Leibniz Health Technologies, Member of the Leibniz Center for Photonics in Infection Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1049-0560","authenticated-orcid":true,"given":"Christoph","family":"Krafft","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02se0t636","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"oe-34-15-27422-R1","doi-asserted-by":"publisher","first-page":"10249","DOI":"10.1021\/acs.jpcb.7b09570","type":"journal-article","volume":"121","author":"Bai","year":"2017","journal-title":"J. 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