Research on Colorization Algorithm for γ-Photon Flow Field Images Using the PECN Model

2025 · doi:10.21203/rs.3.rs-8153875/v1
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Abstract

Abstract γ-photon tomography technology, which leverages the high penetration ability of high-energy γ-photons and their electrical neutrality, has become a groundbreaking non-contact method for monitoring industrial flow fields. However, γ-photon flow fields appear as grayscale images. To enable parameter inversion of these fields, the grayscale images must be colorized into flow field parameter density maps, such as temperature and pressure maps. The DeOldify algorithm is commonly used for coloring grayscale images. γ-photon flow field images exhibit probabilistic imaging features, leading to "color gradient jumps" when DeOldify is applied. The PECN (Physical Enhancement Colorization Network) model forms the basis of the γ-photon flow field image colorization algorithm proposed in this paper to address this challenge. This study introduces a U-Net+GAN framework that integrates multi-scale attention and physical perception augmentation, using ResNet101 as the generator backbone. To enhance physical consistency and reduce color gradient jumps, the discriminator adaptively fuses global and local information across channels and spatial dimensions and incorporates improved blocks to better distinguish boundaries and textures. A flow field condition monitoring experiment was performed on the SW60B turbojet engine at 50% throttle using the Gate simulation platform to evaluate the algorithm’s performance. The validation focused on colorizing the random selected γ-photon flow field images: a large-scale vortex wake and a horizontal wake. The metrics—PSNR, SSIM, FID, and MAE—achieved values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, representing improvements of 4.54%, 2.82%, 5.18%, and 11.16% over the baseline DeOldify algorithm.
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Research on Colorization Algorithm for γ-Photon Flow Field Images Using the PECN Model | 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 Research on Colorization Algorithm for γ-Photon Flow Field Images Using the PECN Model Xiao Hui, Hou LiYing, Song Yulu, Liu Quan, Liu JianTang, Huang ShengJun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153875/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 γ-photon tomography technology, which leverages the high penetration ability of high-energy γ-photons and their electrical neutrality, has become a groundbreaking non-contact method for monitoring industrial flow fields. However, γ-photon flow fields appear as grayscale images. To enable parameter inversion of these fields, the grayscale images must be colorized into flow field parameter density maps, such as temperature and pressure maps. The DeOldify algorithm is commonly used for coloring grayscale images. γ-photon flow field images exhibit probabilistic imaging features, leading to "color gradient jumps" when DeOldify is applied. The PECN (Physical Enhancement Colorization Network) model forms the basis of the γ-photon flow field image colorization algorithm proposed in this paper to address this challenge. This study introduces a U-Net+GAN framework that integrates multi-scale attention and physical perception augmentation, using ResNet101 as the generator backbone. To enhance physical consistency and reduce color gradient jumps, the discriminator adaptively fuses global and local information across channels and spatial dimensions and incorporates improved blocks to better distinguish boundaries and textures. A flow field condition monitoring experiment was performed on the SW60B turbojet engine at 50% throttle using the Gate simulation platform to evaluate the algorithm’s performance. The validation focused on colorizing the random selected γ-photon flow field images: a large-scale vortex wake and a horizontal wake. The metrics—PSNR, SSIM, FID, and MAE—achieved values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, representing improvements of 4.54%, 2.82%, 5.18%, and 11.16% over the baseline DeOldify algorithm. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Physical sciences/Physics Photon imaging Flow field monitoring PECN model Image colorization 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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However, γ-photon flow fields appear as grayscale images. To enable parameter inversion of these fields, the grayscale images must be colorized into flow field parameter density maps, such as temperature and pressure maps. The DeOldify algorithm is commonly used for coloring grayscale images. γ-photon flow field images exhibit probabilistic imaging features, leading to \"color gradient jumps\" when DeOldify is applied. The PECN (Physical Enhancement Colorization Network) model forms the basis of the γ-photon flow field image colorization algorithm proposed in this paper to address this challenge. This study introduces a U-Net+GAN framework that integrates multi-scale attention and physical perception augmentation, using ResNet101 as the generator backbone. To enhance physical consistency and reduce color gradient jumps, the discriminator adaptively fuses global and local information across channels and spatial dimensions and incorporates improved blocks to better distinguish boundaries and textures. A flow field condition monitoring experiment was performed on the SW60B turbojet engine at 50% throttle using the Gate simulation platform to evaluate the algorithm’s performance. The validation focused on colorizing the random selected γ-photon flow field images: a large-scale vortex wake and a horizontal wake. 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