A Hybrid Neural Framework for Robust Image Analysis under Data and Sensor Constraints

A Hybrid Neural Framework for Robust Image Analysis under Data and Sensor Constraints | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2026 V1 Latest version Share on A Hybrid Neural Framework for Robust Image Analysis under Data and Sensor Constraints Authors : Li Weiming 0009-0004-2766-9529 [email protected] and Zhou Xinyi Authors Info & Affiliations https://doi.org/10.22541/au.177499046.64252450/v1 54 views 28 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper proposes a novel, theoretically rigorous deep learning framework designed to maintain high classification and estimation accuracy in environments where standard RGB data is insufficient, corrupted, or unavailable. While state-of-the-art Convolutional Neural Networks (CNNs) excel in varied visual recognition tasks, they often struggle with domain shifts-such as those found in thermal, depth, or low-light imagery-and scenarios characterized by severe data scarcity. We introduce a "Semiparallel Hybrid Architecture" (SHA) that utilizes Cross-Modal Feature Distillation (CMFD) to bridge the semantic gap between varying image modalities. The proposed method employs a dual-stream encoder mechanism fused via a learned Semiparallel Attention Mechanism (SAM), demonstrating superior performance in extracting latent features for biometric security, medical diagnostics, and geometric estimation. Beyond empirical validation, we provide a comprehensive mathematical analysis of the system, employing Information Bottleneck theory to prove that our distillation objective maximizes the relevant mutual information while compressing nuisance variables. We further derive generalization bounds based on Rademacher complexity for the proposed multi-modal hypothesis space. Extensive experiments across three distinct domains-biometrics, medical diagnostics, and monocular depth estimation-reveal that our framework outperforms existing benchmarks, achieving a 14.3% improvement in depth estimation accuracy (RMSE) and a 9.2% increase in F1-score for low-data medical classification tasks compared to standard transfer learning approaches. Supplementary Material File (weiming-hybrid.pdf) Download 495.40 KB Information & Authors Information Version history V1 Version 1 31 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords adversarial robustness biometrics cross-modal distillation depth estimation hybrid neural networks information bottleneck medical diagnostics sensor fusion Authors Affiliations Li Weiming 0009-0004-2766-9529 [email protected] View all articles by this author Zhou Xinyi Qilu Institute of Technology View all articles by this author Metrics & Citations Metrics Article Usage 54 views 28 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Li Weiming, Zhou Xinyi. A Hybrid Neural Framework for Robust Image Analysis under Data and Sensor Constraints. Authorea . 31 March 2026. DOI: https://doi.org/10.22541/au.177499046.64252450/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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