Beyond Wavelets and Deep Learning: A Model-Based Fusion Framework Using Discrete Band-Limited Shearlets

2025 · doi:10.21203/rs.3.rs-7860204/v1
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Abstract

Abstract The limited dynamic range of standard digital imaging sensors often results in under- or over-exposed images, failing to capture the full radiance of a scene as perceived by the Human Visual System (HVS). While deep learning-based Multi-exposure Image Fusion (MEF) methods have recently dominated the field, they often suffer from dependencies on large datasets and lack interpretability. To address this, we propose a novel, model-based MEF framework leveraging the Discrete Band-Limited Shearlet Transform (DBLST). The shearlet transform provides a superior multi-scale and directional representation compared to traditional wavelets, making it exceptionally adept at capturing edges and textures across varying exposure levels—a crucial capability for high-quality fusion. Our method decomposes source images using DBLST and fuses the coefficients through specifically designed rules for low-pass and high-pass components. Extensive experiments on standard datasets demonstrate that the proposed algorithm not only significantly outperforms conventional wavelet-based methods but also achieves competitive performance against recent state-of-the-art approaches in terms of both subjective visual quality and objective metrics (including structural similarity, entropy, and average gradient). The results confirm DBLST as a powerful, efficient, and interpretable tool for high-dynamic-range image rendering, offering a compelling alternative to data-driven deep learning models.
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Beyond Wavelets and Deep Learning: A Model-Based Fusion Framework Using Discrete Band-Limited Shearlets | 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 Article Beyond Wavelets and Deep Learning: A Model-Based Fusion Framework Using Discrete Band-Limited Shearlets Wentao Ji, Xing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7860204/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The limited dynamic range of standard digital imaging sensors often results in under- or over-exposed images, failing to capture the full radiance of a scene as perceived by the Human Visual System (HVS). While deep learning-based Multi-exposure Image Fusion (MEF) methods have recently dominated the field, they often suffer from dependencies on large datasets and lack interpretability. To address this, we propose a novel, model-based MEF framework leveraging the Discrete Band-Limited Shearlet Transform (DBLST). The shearlet transform provides a superior multi-scale and directional representation compared to traditional wavelets, making it exceptionally adept at capturing edges and textures across varying exposure levels—a crucial capability for high-quality fusion. Our method decomposes source images using DBLST and fuses the coefficients through specifically designed rules for low-pass and high-pass components. Extensive experiments on standard datasets demonstrate that the proposed algorithm not only significantly outperforms conventional wavelet-based methods but also achieves competitive performance against recent state-of-the-art approaches in terms of both subjective visual quality and objective metrics (including structural similarity, entropy, and average gradient). The results confirm DBLST as a powerful, efficient, and interpretable tool for high-dynamic-range image rendering, offering a compelling alternative to data-driven deep learning models. Physical sciences/Engineering Physical sciences/Mathematics and computing Multi-exposure image fusion shearlet transform discrete band-limited shearlet transform (DBLST) image enhancement human visual system (HVS) geometric multi-scale analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 22 Oct, 2025 Editor invited by journal 17 Oct, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 14 Oct, 2025 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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