{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:19:30Z","timestamp":1781533170152,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:00:00Z","timestamp":1775260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62571239"],"award-info":[{"award-number":["62571239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["62571239"],"award-info":[{"award-number":["62571239"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016804","name":"Shenzhen Natural Science Foundation","doi-asserted-by":"crossref","award":["JCYJ20250604190734042"],"award-info":[{"award-number":["JCYJ20250604190734042"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Joint Fund of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U2441285"],"award-info":[{"award-number":["U2441285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>\u03b3-photon tomography, which leverages the high penetration and electrical neutrality of high-energy \u03b3-photons, offers a promising non-contact approach for industrial flow field monitoring. However, \u03b3-photon flow-field images are inherently grayscale and exhibit probabilistic statistical imaging characteristics, leading to color banding artifacts when processed by mainstream colorization algorithms like DeOldify, which compromise structural continuity and visual consistency. To address this issue, this paper proposes a Structure Enhancement Colorization Network (SECN) model for \u03b3-photon flow-field image colorization. A U-Net + GAN framework is employed, with ResNet101 as the generator backbone. It integrates structure-aware enhancement and multi-scale attention modules, while the discriminator incorporates enhanced blocks for improved boundary and texture discrimination. By adaptively fusing global\u2013local features across channel and spatial dimensions, the SECN model effectively suppresses color banding artifacts and enhances structural consistency. To validate the effectiveness of the proposed algorithm, two CFD-simulated \u03b3-photon flow-field image colorization scenarios\u2014namely a large-scale vortex wake and a horizontal wake\u2014are used as evaluation targets. In terms of image quality metrics, the proposed colorization algorithm achieves PSNR, SSIM, FID, and MAE values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, corresponding to improvements over DeOldify of 4.54%, 2.82%, 5.18%, and 11.16%. When considering information entropy, the proposed colorization algorithm achieves an average entropy value of 4.0257, marking a 4.44% increase compared to DeOldify\u2019s 3.8543, demonstrating superior information preservation and reduced uncertainty in reconstructing complex probabilistic structures. Furthermore, from the perspective of parameter inversion, the temperature inversion MAPE is 7.60%, which is a significant reduction of 18.42% compared to that of DeOldify.<\/jats:p>","DOI":"10.3390\/e28040414","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T03:11:28Z","timestamp":1775445088000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Colorization Algorithm for \u03b3-Photon Flow Field Images Using the SECN Model"],"prefix":"10.3390","volume":"28","author":[{"given":"Hui","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China"},{"name":"Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen 518110, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liying","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China"},{"name":"Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen 518110, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiantang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103704","DOI":"10.1063\/1.2795648","article-title":"High resolution gamma ray tomography scanner for flow measurement and non-destructive testing applications","volume":"78","author":"Hampel","year":"2007","journal-title":"Rev. Sci. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bruggemann, J., Gross, A., and Pate, S. (2020). Non-intrusive visualization of optically inaccessible flow fields utilizing positron emission tomography. Aerospace, 7.","DOI":"10.3390\/aerospace7050052"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1137\/S0036144598345802","article-title":"Robust parameter estimation in computer vision","volume":"41","author":"Stewart","year":"1999","journal-title":"SIAM Rev."},{"key":"ref_4","first-page":"1453","article-title":"Color and attention for U: Modified multi attention U-Net for a better image colorization","volume":"8","author":"Oliverio","year":"2024","journal-title":"JOIV Int. J. Inform. Vis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhuang, J., Ye, S., Xu, N., Xiao, J., and Peng, C. (2023). Image restoration quality assessment based on regional differential information entropy. Entropy, 25.","DOI":"10.3390\/e25010144"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ke, Z., Zheng, W., Wang, X., and Lin, M. (2024). Information entropy analysis of a PIV image based on wavelet decomposition and reconstruction. Entropy, 26.","DOI":"10.3390\/e26070573"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2900","DOI":"10.1109\/TPAMI.2023.3334614","article-title":"Structured pruning for deep convolutional neural networks: A survey","volume":"46","author":"He","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_11","first-page":"857","article-title":"Self-supervised learning: Generative or contrastive","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102612","DOI":"10.1016\/j.media.2022.102612","article-title":"Cross-modal attention for multi-modal image registration","volume":"82","author":"Song","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6817","DOI":"10.1109\/JSTARS.2022.3198517","article-title":"A CBAM based multiscale transformer fusion approach for remote sensing image change detection","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_17","unstructured":"Wang, Z. (2023). Research on Image Colorization Algorithms Based on Classification Loss Functions. [Master\u2019s Thesis, Anhui University]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, X., Yang, T., Ouyang, W., Ren, P., Li, L., and Xie, X. (2023, January 2\u20136). DDColor: Towards photo-realistic image colorization via dual decoders. Proceedings of the IEEE\/CVF International Conference on Computer Vision(ICCV), Paris, France. Available online: https:\/\/ieeexplore.ieee.org\/document\/10376777.","DOI":"10.1109\/ICCV51070.2023.00037"},{"key":"ref_19","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., and Catanzaro, B. (2018, January 18\u201323). High-resolution image synthesis and semantic manipulation with conditional GANs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vitoria, P., Raad Cisa, L., and Ballester, C. (2020, January 1\u20135). ChromaGAN: Adversarial picture colorization with semantic class distribution. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093389"},{"key":"ref_23","first-page":"5149","article-title":"Meta-learning in neural networks: A Survey","volume":"44","author":"Hospedales","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Kim, Y., Cho, Y., Nguyen, T.-T., Hong, S., and Lee, D. (2022, January 23\u201327). MetaWeather: Few-Shot weather-degraded image restoration. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel."},{"key":"ref_25","unstructured":"Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L. (November, January 27). S4L: Self-supervised semi-supervised learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"21443","DOI":"10.1038\/s41598-025-07107-1","article-title":"Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures","volume":"15","author":"Xiao","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11\u201317). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00933"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_29","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017, January 4\u20139). Improved training of Wasserstein GANs. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/4\/414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T04:13:10Z","timestamp":1775448790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/4\/414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,4]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["e28040414"],"URL":"https:\/\/doi.org\/10.3390\/e28040414","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,4]]}}}