{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:24:46Z","timestamp":1772987086002,"version":"3.50.1"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Ultrasound"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Image denoising is a crucial pre\u2010processing technique in retinal optical coherence tomography image compression, but existing methods struggle with signal\u2010dependent noise and do not consider hybridized low\u2010contrast residual noise (HLRN), failing to gather information from images. Thus, the novel\n                    <jats:italic>Smooth Gyrated Texel Quadrivium Network<\/jats:italic>\n                    (\n                    <jats:italic>SGTQN<\/jats:italic>\n                    ) is proposed to reduce noise and collect self\u2010sufficient information. In the SGTQN, the novel Additive Ascombe Smooth Sifter converts Poisson noise into Gaussian noise using the Ascombe Transform and removes unwanted Gaussian noise and the HLRN by hybridized noise removal, thus effectively gathering useful information from the image. After denoising, existing segmentation methodologies neglect the retinal nerve deviation value, creating a poor self\u2010explanatory image. Thus, a novel Improvised Gyrated Alexa Net incorporates the Standardized Gyrated Layer, which considers the deviation values, thus generating a self\u2010explanatory segmented image. Furthermore, many existing compression methods failed to achieve a higher quality image due to their non\u2010uniform compression. The Texel Quadrivium Convolutional Network modifies the pooling layer into a Texel Quadrivium Layer to perform uniform compression and uses adjuvant vector coordinates to generate a high\u2010resolution compressed image. This proposed model provides high\u2010quality image compression with reduced noise, with a high accuracy of 95% and a lower mean square error of 0.02.\n                  <\/jats:p>","DOI":"10.1002\/jcu.70187","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T05:41:56Z","timestamp":1769406116000},"page":"698-720","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smooth Gyrated Texel Quadrivium Network for\n                    <scp>3D<\/scp>\n                    Retinal\n                    <scp>OCT<\/scp>\n                    Image Compression"],"prefix":"10.1002","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9375-5692","authenticated-orcid":false,"given":"N.","family":"Nanthini","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology  Coimbatore Tamilnadu India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Sasipriya","sequence":"additional","affiliation":[{"name":"Department of ECE Sreenidhi Institute of Science and Technology  Hyderabad Telangana India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Ramkumar","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Department Sri Krishna College of Engineering and Technology  Coimbatore Tamilnadu India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3289162"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.541655"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Chen Y. M.Nguyen Y.Levy E.Bayram M.Noga andK.Punithakumar.2024.A Comprehensive Study of Conditional Generative Adversarial Networks for Noise Reduction in Optical Coherence Tomography. 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