HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images

HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images | 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 HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images Gaurav Choudhary, Dinesh Sethi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3772668/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract Image fusion (IF) due to its cost-effectiveness and applicability in a broader range of applications makes it an emerging area in research. However, it is seen from the literature that most of the existing fusion algorithms are application-specific. As a result, the results obtained for different applications are limited. So, in this work, we propose an effective algorithm for better outcomes for different applications. For this, an adaptive image decomposition tool known as Hilbert vibration decomposition (HVD) is used. HVD decomposes an image into instantaneous energy components having amplitudes (image amplitudes) and frequencies. Unlike traditional multi-scale decomposition, the adaptive decomposition strategy used by HVD does not require any fixed cut-off frequency or pre-defined function basis and offers better spatial resolution. Then, we compute instantaneous detailed image amplitudes that generally contain significant information. These are obtained by subtracting the instantaneous image amplitudes from the source images. Further, we find the optimized weights with the help of a statistical approach, i.e., by using unbiased estimates and eigenvalues related to these instantaneous detailed image amplitudes. After this computation, the optimized weights are integrated with source images to generate the final fused image. The simulation of the proposed work is carried out in MATLAB software for multi-focus, medical, and visible-infrared (VI-IR) image samples and compared with existing methods. It is seen that in comparison to traditional as well as some deep learning-based fusion works, the proposed work not only provides better/comparative outputs qualitatively and quantitatively but there is also less computational time complexity. Image fusion Hilbert vibration decomposition (HVD) instantaneous image amplitudes evaluation metrics Full Text Additional Declarations No competing interests reported. Tables 1-6 is available in the Supplementary Files section. Supplementary Files Tables.docx AdditionalResults.docx Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted Reviewers agreed at journal 24 Dec, 2023 Reviewers invited by journal 19 Dec, 2023 Submission checks completed at journal 18 Dec, 2023 Editor assigned by journal 18 Dec, 2023 First submitted to journal 18 Dec, 2023 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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