HazeAway: A Convolutional Approach to Single-Image Haze Removal

HazeAway: A Convolutional Approach to Single-Image Haze Removal | 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 HazeAway: A Convolutional Approach to Single-Image Haze Removal Jiyoung Kim, Palash Ingle This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5584935/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 existence of particles in the air cause haze, and this haze or fog cause degraded visibility in the captured shot from the camera. The non-uniform distribution of these particles, along with smoke, low light and pollution in the atmosphere, makes haze removal difficult in the real world images. The core computer vision tasks struggle with hazy images due to the lack of detail and poor visibility. The existing method relies on a transmission map in amalgamation with the atmospheric scattering input images to reconstruct a haze-free depiction. We suggested a single-image convolutions neural network that removes the haze present in the image and improves the perceptual quality by enhancing visibility. We used U-Net-like architecture with an encoder, bottleneck and decoder coupled with skip connections. In our experiment, we demonstrated the results on various benchmark dataset and compared our results with existing approaches. Additionally we compared the results from our network training on different image representations RGB verses YCbCr. The proposed method is straightforward and miniature yet still gives near state-of-the-art results. Haze removal De-hazing U-Net Visibility enhancement Convolution neural 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-5584935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402293666,"identity":"1abcb6c8-cad4-4e7d-abf1-25b466038772","order_by":0,"name":"Jiyoung Kim","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Jiyoung","middleName":"","lastName":"Kim","suffix":""},{"id":402293667,"identity":"e340b16c-089d-4128-a045-ffb2155d7f44","order_by":1,"name":"Palash Ingle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFAC5gYGxgYbIAbzEojRwgjSkka6lsMwHhFazNkPNn78ueO8PfOMBMYPPxjS8glqsexJbJbmPXM7sXFGArNkD0OOZQMhLQYHEhukGdtuJzDOSGCQZmCoMCBoi8H5h80/f7adswdqYf5NnJYbiW0SvG0HGIEOYwPakkNYi+WMh23WvG3JiY09D9ssewzSCGsx508+fPNnm529YXvy4Rs/KpKJcBiMYdgAikzCGpDUyBOheBSMglEwCkYoAACHBzy+XqgsqAAAAABJRU5ErkJggg==","orcid":"","institution":"Sejong University","correspondingAuthor":true,"prefix":"","firstName":"Palash","middleName":"","lastName":"Ingle","suffix":""}],"badges":[],"createdAt":"2024-12-05 08:08:33","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-5584935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5584935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74023299,"identity":"fde08cf1-cfc8-435e-a862-619ff26046f8","added_by":"auto","created_at":"2025-01-17 05:36:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34252628,"visible":true,"origin":"","legend":"","description":"","filename":"HazeAway.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5584935/v1_covered_7e63df60-9d07-42c9-8260-f5d4a904b9c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HazeAway: A Convolutional Approach to Single-Image Haze Removal","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Haze removal, De-hazing, U-Net, Visibility enhancement, Convolution neural networks","lastPublishedDoi":"10.21203/rs.3.rs-5584935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5584935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The existence of particles in the air cause haze, and this haze or fog cause degraded visibility in the captured shot from the camera. The non-uniform distribution of these particles, along with smoke, low light and pollution in the atmosphere, makes haze removal difficult in the real world images. The core computer vision tasks struggle with hazy images due to the lack of detail and poor visibility. The existing method relies on a transmission map in amalgamation with the atmospheric scattering input images to reconstruct a haze-free depiction. We suggested a single-image convolutions neural network that removes the haze present in the image and improves the perceptual quality by enhancing visibility. We used U-Net-like architecture with an encoder, bottleneck and decoder coupled with skip connections. In our experiment, we demonstrated the results on various benchmark dataset and compared our results with existing approaches. Additionally we compared the results from our network training on different image representations RGB verses YCbCr. The proposed method is straightforward and miniature yet still gives near state-of-the-art results.","manuscriptTitle":"HazeAway: A Convolutional Approach to Single-Image Haze Removal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 05:27:53","doi":"10.21203/rs.3.rs-5584935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"47c2f6a9-73f8-487c-aecf-683291e99a62","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T05:27:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 05:27:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5584935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5584935","identity":"rs-5584935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}