A Novel Detail Enhancement Method for Industrial Radiography Based on Scattering Component Separation

A Novel Detail Enhancement Method for Industrial Radiography Based on Scattering Component Separation | 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 A Novel Detail Enhancement Method for Industrial Radiography Based on Scattering Component Separation Fayu Chen, Guancheng Lu, Wei Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8839515/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Industrial digital radiography (DR) is a critical nondestructive evaluation technology used in safety-sensitive manufacturing industries. The high dynamic range of 16-bit DR images, while capturing subtle material discontinuities, poses a formidable challenge for defect analysis because of the presence of scattering-induced noise and low contrast defect signatures. Conventional enhancement techniques often fail to balance noise suppression with detail preservation, whereas deep learning methods require extensive annotated data and lack physical interpretability. To address these limitations, this study presents a novel physics-aware enhancement framework based on a radiation-matter interaction model. The core innovation involves estimating and separating the scattering component from the total detected intensity to recover the direct transmission signal. The proposed method employs a multistage architecture that sequentially performs attenuation estimation, scattering modelling, residual scattering removal, edge sharpening, and adaptive-detail contrast enhancement. Experiments were conducted on 60 industrial DR images obtained from weld inspections of ship plates, boilers, and oil pipelines. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spatial Frequency (SF) improvement metrics demonstrated that the proposed method outperformed conventional techniques (global histogram equalization, contrast-limited adaptive histogram equalization), a transform-domain method (Discrete Wavelet Transform), and a convolutional neural network (CNN)-based model. The framework achieves the highest average PSNR (up to 26.96), SSIM (up to 0.76), and improved SF (up to 1203.18), as well as the lowest variances in these metrics. It also delivers substantial relative improvements over traditional methods (for example, PSNR gains over HE exceeding 90%) and consistent incremental gains over the CNN benchmark. This study contributes a physically interpretable, training-free solution that effectively enhances defect visibility and structural fidelity in industrial DR imagery, offering a reliable tool for practical nondestructive evaluation. Physical sciences/Engineering Physical sciences/Mathematics and computing Industrial digital radiography image enhancement scattering suppression physics‑aware model nondestructive evaluation Full Text Additional Declarations No competing interests reported. Supplementary Files SourceCode.zip Video.mp4 TestedData.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 19 Feb, 2026 Editor invited by journal 17 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 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. 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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-8839515","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604606587,"identity":"715aa80b-eafd-456b-bad4-4495ebd30d1c","order_by":0,"name":"Fayu Chen","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"prefix":"","firstName":"Fayu","middleName":"","lastName":"Chen","suffix":""},{"id":604606588,"identity":"6de4c95d-5232-42d4-8d0b-2888f9e4bcb8","order_by":1,"name":"Guancheng 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The high dynamic range of 16-bit DR images, while capturing subtle material discontinuities, poses a formidable challenge for defect analysis because of the presence of scattering-induced noise and low contrast defect signatures. Conventional enhancement techniques often fail to balance noise suppression with detail preservation, whereas deep learning methods require extensive annotated data and lack physical interpretability. To address these limitations, this study presents a novel physics-aware enhancement framework based on a radiation-matter interaction model. The core innovation involves estimating and separating the scattering component from the total detected intensity to recover the direct transmission signal. The proposed method employs a multistage architecture that sequentially performs attenuation estimation, scattering modelling, residual scattering removal, edge sharpening, and adaptive-detail contrast enhancement. 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