Grayscale mapping of infrared images based on end-to-end deep neural networks

Grayscale mapping of infrared images based on end-to-end deep neural networks | 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 Grayscale mapping of infrared images based on end-to-end deep neural networks Lin Cheng, Wenqing Hong, Xiaodong Wang, Chen Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890488/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 The grayscale mapping of infrared images is an important research direction in the field of infrared imaging. A fast and scene-adaptive grayscale mapping method is crucial for visualizing high dynamic range original infrared images in various standard dynamic range output devices, such as printers and standard monitors. At present, mainstream grayscale mapping methods can only handle high dynamic range images in limited scenes and require extensive parameter adjustments to generate high-quality mapping results. In this paper, we propose a fast, parameter-free, and scene-adaptive grayscale mapping method to address this issue, which can achieve high subjective quality mapping results. Our model not only adapts to various categories of scenes, but also resolves the issues of insufficient contrast and significant loss of details in the grayscale mapping of high dynamic range infrared images. We explored the different impacts of the loss functions and normalization layers in the model on the mapping effect, and ultimately adopted L 1 loss, perceptual loss, and batch normalization to accomplish our task. To ensure the production of high-quality mapping results, we used the objective metric of high dynamic range image quality assessment, specifically the tone mapping image quality index, to identify target images for training our model. We evaluated our results from both quantitative and qualitative perspectives, showcasing the high-quality output images generated by our model in a wide range of real-world scenarios. This substantiates the superiority of our approach. high dynamic range infrared images grayscale mapping generative adversarial networks Full Text Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3890488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269706401,"identity":"5a118f39-9b9b-4fe2-884b-55dd2c0cad4b","order_by":0,"name":"Lin Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCRBRIMHAz95++MEHAxs7IrUYSDBI9pxJM5xRkJZMrBYguuFgIM3z4RBjAyEd/LObjz38YmCRx3CDIcHYxuAAMwP74aMb8Fpy51i6sYyBRDHj7MYDj3MM7vAx8KSl3cCnxUAix0xawkAisVnmQIJxjsEzZgYJHjMCWvK/gbW0SSQYSFsYHGZsIKwlh03yA1BLD0gLAzFaJG6kmQFVSiTO4AEGco9BWjIbIb/wz0h+Jvmjoi5x/3FgVP74Y2PHz374GF4tIMDMg8xjI6QcBBh/EKNqFIyCUTAKRi4AABiGSHO7Id2UAAAAAElFTkSuQmCC","orcid":"","institution":"Kunming Institute of Physics","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Cheng","suffix":""},{"id":269706402,"identity":"d42d5bfc-81a7-4e3d-b6bb-f6b0f9e01937","order_by":1,"name":"Wenqing Hong","email":"","orcid":"","institution":"Kunming Institute of Physics","correspondingAuthor":false,"prefix":"","firstName":"Wenqing","middleName":"","lastName":"Hong","suffix":""},{"id":269706403,"identity":"ce41c0be-719e-4b67-a4d9-e3021cd9fe6e","order_by":2,"name":"Xiaodong Wang","email":"","orcid":"","institution":"Kunming Institute of Physics","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Wang","suffix":""},{"id":269706404,"identity":"c320943b-7841-472a-804c-a07d65e3b6b4","order_by":3,"name":"Chen Zhang","email":"","orcid":"","institution":"Kunming Institute of Physics","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-23 08:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51575198,"identity":"e4f42b14-12c0-48e7-93ea-54b77a016a86","added_by":"auto","created_at":"2024-02-24 04:08:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":754634,"visible":true,"origin":"","legend":"","description":"","filename":"Grayscalemappingofinfraredimagesbasedonendtoenddeepneuralnetworks.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890488/v1_covered_0f2f2bf4-f7d6-4402-bced-73a0b9daa05c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Grayscale mapping of infrared images based on end-to-end deep neural networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"high dynamic range, infrared images, grayscale mapping, generative adversarial networks","lastPublishedDoi":"10.21203/rs.3.rs-3890488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe grayscale mapping of infrared images is an important research direction in the field of infrared imaging. A fast and scene-adaptive grayscale mapping method is crucial for visualizing high dynamic range original infrared images in various standard dynamic range output devices, such as printers and standard monitors. At present, mainstream grayscale mapping methods can only handle high dynamic range images in limited scenes and require extensive parameter adjustments to generate high-quality mapping results. In this paper, we propose a fast, parameter-free, and scene-adaptive grayscale mapping method to address this issue, which can achieve high subjective quality mapping results. Our model not only adapts to various categories of scenes, but also resolves the issues of insufficient contrast and significant loss of details in the grayscale mapping of high dynamic range infrared images. 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