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Gleason grading serves as the primary criterion for assessing PCa. However, obtaining high signal\u2010to\u2010noise ratio (SNR) SHG images requires prolonged acquisition, limiting clinical feasibility. In this study, rapid SHG imaging was combined with a deep\u2010learning\u2010based denoising network, Selective Residual M\u2010Net (SRMNet), to reconstruct high\u2010fidelity images from low\u2010SNR inputs. The denoising procedure markedly enhanced image quality while preserving collagen alignment features essential for quantitative assessment. The enhanced image fidelity enabled more robust extraction of collagen orientation metrics under low\u2010SNR conditions, facilitating accurate assessment of stromal organization with reduced acquisition time. These results highlight that deep\u2010learning\u2010assisted SHG imaging provides an effective strategy for reliable collagen orientation analysis in PCa, supporting the development of rapid and objective optical histopathology.<\/jats:p>","DOI":"10.1002\/jbio.70233","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:20:52Z","timestamp":1769995252000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prostate Cancer Characterization Based on Rapid\n                    <scp>SHG<\/scp>\n                    Imaging of Collagen Fiber Combined With Denoising Algorithm"],"prefix":"10.1002","volume":"19","author":[{"given":"Jia","family":"He","sequence":"first","affiliation":[{"name":"Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education 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