Multi-focus image fusion based on dynamic threshold neural P systems and difference of gaussian clarity

Multi-focus image fusion based on dynamic threshold neural P systems and difference of gaussian clarity | 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 Multi-focus image fusion based on dynamic threshold neural P systems and difference of gaussian clarity Xu-Jie Duan, Xiao-Ting Guo, Hong-Liang Wang, Xiu-Yuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7708149/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 To address the limitations of optical lens depth of field and integrate complementary information, Multi-Focus Image Fusion merges multiple images of the same scene taken at different focal points into a fully clear image. Although decision-map methods are commonly used in MFIF, accurately defining boundaries between focused and defocused areas remains a challenge. The Dynamic Threshold Neural P system, a distributed parallel computing model inspired by the cross-cortical model. Using dynamic thresholds and spiking mechanisms, it enables us to improve discrimination between areas in focus and out of focus. Building an enhanced framework called the Multi-scale Gaussian Contrast Synergistic Dynamic Threshold Neural P System (MGCS-DTNP) has been proposed. This method employs Gaussian contrast filtering to extract gradient features, leveraging the fact that focused regions exhibit sharper gradients. These features serve as input stimuli to the DTNP system, generating an initial decision map based on diffusion time parameters. After post-processing with small-area removal and median filtering, a refined decision map is produced. The final fused image is reconstructed by integrating regions from the source images according to this map. Experiments on the Lytro and MFFW datasets demonstrate that MGCS-DTNP outperforms 12 existing algorithms across six evaluation metrics, showing superior visual quality and fusion performance. Multi-focus image fusion Gaussian contrast filter function Clarity calculation Dynamic threshold neural P systems Decision map 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. 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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-7708149","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526256406,"identity":"f50ff87c-7e26-4603-9300-4fd3179cccdc","order_by":0,"name":"Xu-Jie Duan","email":"","orcid":"","institution":"North University of China","correspondingAuthor":false,"prefix":"","firstName":"Xu-Jie","middleName":"","lastName":"Duan","suffix":""},{"id":526256407,"identity":"1a4e4b2f-4a6e-4675-8258-c571fbf41980","order_by":1,"name":"Xiao-Ting 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clarity","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":"Multi-focus image fusion, Gaussian contrast filter function, Clarity calculation, Dynamic threshold neural P systems, Decision map","lastPublishedDoi":"10.21203/rs.3.rs-7708149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7708149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the limitations of optical lens depth of field and integrate complementary information, Multi-Focus Image Fusion merges multiple images of the same scene taken at different focal points into a fully clear image. Although decision-map methods are commonly used in MFIF, accurately defining boundaries between focused and defocused areas remains a challenge. The Dynamic Threshold Neural P system, a distributed parallel computing model inspired by the cross-cortical model. Using dynamic thresholds and spiking mechanisms, it enables us to improve discrimination between areas in focus and out of focus. Building an enhanced framework called the Multi-scale Gaussian Contrast Synergistic Dynamic Threshold Neural P System (MGCS-DTNP) has been proposed. This method employs Gaussian contrast filtering to extract gradient features, leveraging the fact that focused regions exhibit sharper gradients. These features serve as input stimuli to the DTNP system, generating an initial decision map based on diffusion time parameters. After post-processing with small-area removal and median filtering, a refined decision map is produced. The final fused image is reconstructed by integrating regions from the source images according to this map. Experiments on the Lytro and MFFW datasets demonstrate that MGCS-DTNP outperforms 12 existing algorithms across six evaluation metrics, showing superior visual quality and fusion performance.\u003c/p\u003e","manuscriptTitle":"Multi-focus image fusion based on dynamic threshold neural P systems and difference of gaussian clarity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 08:30:07","doi":"10.21203/rs.3.rs-7708149/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":"4bb8597b-cbd7-4658-b7e1-49bc98884c92","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T12:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 08:30:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7708149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7708149","identity":"rs-7708149","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}