{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:53:26Z","timestamp":1785434006902,"version":"3.56.0"},"reference-count":22,"publisher":"Optica Publishing Group","issue":"11","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR-OA"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4702902"],"award-info":[{"award-number":["2022YFB4702902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62275023"],"award-info":[{"award-number":["62275023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4232077"],"award-info":[{"award-number":["4232077"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Opt. Express"],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:p>\n                    Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology, capable of non-invasive, high-resolution imaging of retinal tissues. However, OCT images are often degraded by speckle noise, resulting in poor image quality. Deep learning-based denoising models have become the mainstream approach, but existing methods tend to oversmooth images and lose high-frequency details, making it difficult to recover the true retinal structure. This paper proposes a high-frequency enhanced diffusion model based on the cold diffusion framework, named THFN-OCT (time-enhanced high-frequency network for OCT denoising). The model decouples frequency-domain information and processes it separately, with cross-domain connections to preserve high-frequency details while ensuring denoising performance. In addition, considering the different impacts of each diffusion timestep on frequency components, we design a timestep-aware attention module that uses the timestep\n                    <jats:italic>t<\/jats:italic>\n                    to guide the reconstruction. Experiments on two public OCT retinal denoising datasets and one private dataset show that the proposed method outperforms existing denoising algorithms.\n                  <\/jats:p>","DOI":"10.1364\/boe.575221","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T15:00:10Z","timestamp":1757516410000},"page":"4571","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Time-step encoded high-frequency enhanced diffusion model for OCT retinal image denoising"],"prefix":"10.1364","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7703-4514","authenticated-orcid":true,"given":"Boyu","family":"Yang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01skt4w74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"The National Key Laboratory on Near-surface Detection"},{"name":"Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7031-9709","authenticated-orcid":true,"given":"Yong","family":"Huang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01skt4w74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"The National Key Laboratory on Near-surface Detection"},{"name":"Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxiong","family":"Xie","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01skt4w74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"The National Key Laboratory on Near-surface Detection"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01skt4w74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"The National Key Laboratory on Near-surface Detection"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shisen","family":"Jia","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Applied Optics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Hao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01skt4w74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"The National Key Laboratory on Near-surface Detection"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"boe-16-11-4571-R1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/RBME.2021.3110958","type":"journal-article","volume":"16","author":"Pan","year":"2023","journal-title":"IEEE Rev. 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