Enhancing Medical Images Quality Using Vision Transformer Framework

Enhancing Medical Images Quality Using Vision Transformer Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Medical Images Quality Using Vision Transformer Framework Muhammad Hamza Farooq, Thamer Alshammari, Muhammad Usman Ghani Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7173744/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract High resolution images are crucial for precise diagnosis and efficient treatment planning in medical imaging. However, prevalence of low-resolution images remains a significant challenge, often limiting the detail and clarity necessary for reliable clinical evaluations. To address this issue, we applied the Vision Transformer Auto Encoder (ViTAE), a specialized Convolutional Neural Network CNN model designed for image enhancement. The study’s dataset, which included a range of medical imaging scenarios, was gathered locally from a computed tomography (CT) scan lab. Over a series of training epochs, the Vision Transformer Auto Encoder (ViTAE) exhibited consistent improvements in peak signal to noise ratio (PSNR), ultimately achieving PSNR of 43.06 decibels dB and Structural Similarity Index Measure SSIM of 0.983. Our proposed model ViTAE also outperforms the other Information eXtraction from Images IXI dataset having a PSNR of 43.72 decibels (dB) and SSIM 0.984 respectively. By optimizing its convolutional layers to extract and refine features from the input images, model progressively enhanced its ability to reconstruct and clarify images. These results underscore potential of ViTAE to significantly improve quality of medical images, offering a promising solution to overcome limitations of low resolution medical imaging. Vision Transformer Auto Encoder Peak Signal-to-Noise Ratio high-resolution medical imaging Structural Similarity Index Measure CT Scans Convolutional Neural Network Full Text Additional Declarations No competing interests reported. "Institutional Review Board Statement: This dataset was provided by Al-Nasr Diagnostic Center, Lahore for research and development purposes. The diagnostic center has reviewed and approved the anonymized data for academic and scientific use. All personally identifiable information has been removed prior to data sharing. The dataset does not contain any metadata or patient identifiers. Patient Consent Statement: All patients undergoing CT brain imaging at Al-Nasr Diagnostic Center provided general consent for their anonymized data to be used in research and development. This consent includes permission to use anonymized images in academic publications and open-access datasets, in accordance with ethical research standards. Dataset Use and Ethics: This dataset is shared solely for non-commercial, academic, and research purposes. It complies with ethical principles outlined in the Declaration of Helsinki. The authors and dataset contributors" Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7173744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493260187,"identity":"131c62e9-15f2-4060-9b41-f9c036c73985","order_by":0,"name":"Muhammad Hamza Farooq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACAwglB6E+HJAA0xIIGZxajMEk4wyStTDzHGAgrMWc/XTyhx8MBvLm7c0Hb9ucsZCXb2A+eJuH4Y4xLi2WPbkbDHsYDAznnDmWbJ1zQ8JwwwG2ZGsehmdmOB12IHdDAg/DH8YZEjlm0jkfJBIMGHjMpHkYDtvg1HL+7YaDfxgM7MFaLIBa5Bv4v+HXciN3YzMPg0EiWAvDDYkEhgM8bCAtuB124+1mZhkDg+QZPMeSLXvOAP1ymM3Yco7BM5zeNzifu/njmwoD2xnszQdv/DhWJy/f3vzwxpuKO4YNuPRANEIoSDwyQ4IFrwY4kEBiE6llFIyCUTAKRgIAAGsMU33iThYJAAAAAElFTkSuQmCC","orcid":"","institution":"National Centre of Artificial Intelligence, UET LAHORE","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Hamza","lastName":"Farooq","suffix":""},{"id":493260188,"identity":"cc09f103-f0e7-4775-b4fb-023974597645","order_by":1,"name":"Thamer Alshammari","email":"","orcid":"","institution":"Saudi Electronic University","correspondingAuthor":false,"prefix":"","firstName":"Thamer","middleName":"","lastName":"Alshammari","suffix":""},{"id":493260189,"identity":"31baa659-6b42-42e1-af8d-47c61fefe363","order_by":2,"name":"Muhammad Usman Ghani Khan","email":"","orcid":"","institution":"National Centre of Artificial Intelligence, UET LAHORE","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Usman Ghani","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2025-07-21 06:08:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7173744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7173744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92620664,"identity":"7c2803fd-3773-4453-a60b-454c254f4d4d","added_by":"auto","created_at":"2025-10-01 19:16:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1152974,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingMIImagesSpringernature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7173744/v1_covered_4e567ace-1c9e-4df0-b48c-ad0f103f4aee.pdf"}],"financialInterests":"\u003cp\u003eNo competing interests reported.\u003c/p\u003e\n\u003cp\u003e\"Institutional Review Board Statement: This dataset was provided by Al-Nasr Diagnostic Center, Lahore for research and development purposes. 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