{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:12:05Z","timestamp":1757617925091,"version":"3.44.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Postgraduate Research Practice Innovation Program of Jiangsu Province","award":["SJCX23_0573"],"award-info":[{"award-number":["SJCX23_0573"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No. 62101268","Grant No.82204770","Grant No. 62176126"],"award-info":[{"award-number":["Grant No. 62101268","Grant No.82204770","Grant No. 62176126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Youth Science Foundation of Jiangsu Province","award":["Grant No.BK20210696"],"award-info":[{"award-number":["Grant No.BK20210696"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["Grant No.BK20230095"],"award-info":[{"award-number":["Grant No.BK20230095"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s00138-025-01702-y","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T09:30:31Z","timestamp":1747301431000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Meta-learning enhanced global\u2013local feature fusion for image quality assessment"],"prefix":"10.1007","volume":"36","author":[{"given":"Nengxin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xichen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"5","key":"1702_CR1","doi-asserted-by":"publisher","first-page":"6801","DOI":"10.1007\/s11042-022-11924-1","volume":"81","author":"K Geng","year":"2022","unstructured":"Geng, K., Dong, G., Huang, W.: Robust dual-modal image quality assessment aware deep learning network for traffic targets detection of autonomous vehicles. Multimed. Tools Appl. 81(5), 6801\u20136826 (2022)","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"1702_CR2","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"1702_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/97.995823","volume":"9","author":"Z Wang","year":"2002","unstructured":"Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81\u201384 (2002)","journal-title":"IEEE Signal Process. Lett."},{"key":"1702_CR4","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, IEEE, pp 1398\u20131402 (2003)"},{"key":"1702_CR5","doi-asserted-by":"crossref","unstructured":"Cao, Y., Wan, Z., Ren, D., et\u00a0al.: Incorporating semi-supervised and positive-unlabeled learning for boosting full reference image quality assessment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5851\u20135861 (2022)","DOI":"10.1109\/CVPR52688.2022.00576"},{"key":"1702_CR6","doi-asserted-by":"crossref","unstructured":"Lao, S., Gong, Y., Shi, S., et\u00a0al.: Attentions help cnns see better: Attention-based hybrid image quality assessment network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1140\u20131149 (2022)","DOI":"10.1109\/CVPRW56347.2022.00123"},{"issue":"12","key":"1702_CR7","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695\u20134708 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"1702_CR8","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1109\/TIP.2012.2190086","volume":"21","author":"P Ye","year":"2012","unstructured":"Ye, P., Doermann, D.: No-reference image quality assessment using visual codebooks. IEEE Trans. Image Process. 21(7), 3129\u20133138 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"1702_CR9","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., et\u00a0al.: Convolutional neural networks for no-reference image quality assessment. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1733\u20131740 (2014)","DOI":"10.1109\/CVPR.2014.224"},{"issue":"1","key":"1702_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/TNNLS.2018.2829819","volume":"30","author":"J Kim","year":"2018","unstructured":"Kim, J., Nguyen, A.D., Lee, S.: Deep cnn-based blind image quality predictor. IEEE Trans. Neural Netw. Learn. Syst. 30(1), 11\u201324 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"1702_CR11","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TIP.2017.2760518","volume":"27","author":"S Bosse","year":"2017","unstructured":"Bosse, S., Maniry, D., M\u00fcller, K.R., et al.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27(1), 206\u2013219 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"1702_CR12","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","volume":"30","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Ma, K., Yan, J., Deng, D., Wang, Z.: Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Trans. Circuits Syst. Video Technol. 30(1), 36\u201347 (2020). https:\/\/doi.org\/10.1109\/TCSVT.2018.2886771","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1702_CR13","doi-asserted-by":"crossref","unstructured":"Zhu, H., Li, L., Wu, J., et\u00a0al.: Metaiqa: Deep meta-learning for no-reference image quality assessment. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14143\u201314152 (2020)","DOI":"10.1109\/CVPR42600.2020.01415"},{"key":"1702_CR14","volume-title":"Blindly assess image quality in the wild guided by a self-adaptive hyper network-supplementary material","author":"S Su","year":"2020","unstructured":"Su, S., Yan, Q., Zhu, Y., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network-supplementary material. Northwestern Polytechnical University, Xi\u2019an, China (2020)"},{"key":"1702_CR15","unstructured":"Vaswani, A.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"key":"1702_CR16","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, T., Shi, S., et\u00a0al.: Maniqa: Multi-dimension attention network for no-reference image quality assessment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1191\u20131200 (2022)","DOI":"10.1109\/CVPRW56347.2022.00126"},{"issue":"2","key":"1702_CR17","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1609\/aaai.v37i2.25302","volume":"37","author":"G Qin","year":"2023","unstructured":"Qin, G., Hu, R., Liu, Y., Zheng, X., Liu, H., Li, X., Zhang, Y.: Data-efficient image quality assessment with attention-panel decoder. Proc. AAAI Conf. Artif. Intell. 37(2), 2091\u20132100 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i2.25302","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1702_CR18","doi-asserted-by":"crossref","unstructured":"Golestaneh, S.A., Dadsetan, S., Kitani, K.M.: No-reference image quality assessment via transformers, relative ranking, and self-consistency. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1220\u20131230 (2022)","DOI":"10.1109\/WACV51458.2022.00404"},{"key":"1702_CR19","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y., et\u00a0al.: Musiq: Multi-scale image quality transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5148\u20135157 (2021)","DOI":"10.1109\/ICCV48922.2021.00510"},{"issue":"3s","key":"1702_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3468872","volume":"17","author":"C Yan","year":"2021","unstructured":"Yan, C., Teng, T., Liu, Y., Zhang, Y., Wang, H., Ji, X.: Precise no-reference image quality evaluation based on distortion identification. ACM Trans. Multimed. Comput., Commun., Appl. 17(3s), 1\u201321 (2021). https:\/\/doi.org\/10.1145\/3468872","journal-title":"ACM Trans. Multimed. Comput., Commun., Appl."},{"key":"1702_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.inffus.2021.09.005","volume":"79","author":"H Chen","year":"2022","unstructured":"Chen, H., He, X., Qing, L., Wu, Y., Ren, C., Sheriff, R.E., Zhu, C.: Real-world single image super-resolution: A brief review. Inf. Fusion 79, 124\u2013145 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2021.09.005","journal-title":"Inf. Fusion"},{"issue":"12","key":"1702_CR22","doi-asserted-by":"publisher","first-page":"8512","DOI":"10.1109\/TCSVT.2021.3112197","volume":"32","author":"L Li","year":"2021","unstructured":"Li, L., Song, T., Wu, J., et al.: Blind image quality index for authentic distortions with local and global deep feature aggregation. IEEE Trans. Circuits Syst. Video Technol. 32(12), 8512\u20138523 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"1702_CR23","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/MSP.2017.2736018","volume":"34","author":"J Kim","year":"2017","unstructured":"Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., Bovik, A.C.: Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment. IEEE Signal Process. Mag. 34(6), 130\u2013141 (2017). https:\/\/doi.org\/10.1109\/MSP.2017.2736018","journal-title":"IEEE Signal Process. Mag."},{"key":"1702_CR24","unstructured":"Li, Z., Zhou, F., Chen, F., et\u00a0al.: Meta-sgd: Learning to learn quickly for few-shot learning. arxiv 2017. arXiv preprint arXiv:1707.09835 (2017)"},{"key":"1702_CR25","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, PMLR, pp 1126\u20131135 (2017)"},{"key":"1702_CR26","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1007\/978-3-030-58523-5_44","volume-title":"Computer Vision\u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII","author":"C Liu","year":"2020","unstructured":"Liu, C., Wang, Z., Sahoo, D., Fang, Y., Zhang, K., Hoi, S.: Adaptive Task Sampling for\u00a0Meta-learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision\u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII, pp. 752\u2013769. Springer International Publishing, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_44"},{"key":"1702_CR27","unstructured":"Zhou, P., Yuan, X., Xu, H., et\u00a0al.: Efficient meta learning via minibatch proximal update. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"1702_CR28","first-page":"57","volume":"30","author":"N Ponomarenko","year":"2015","unstructured":"Ponomarenko, N., Jin, L., Ieremeiev, O., et al.: Image database tid2013: Peculiarities, results and perspectives. Signal Process.: Image Commun. 30, 57\u201377 (2015)","journal-title":"Signal Process.: Image Commun."},{"key":"1702_CR29","doi-asserted-by":"crossref","unstructured":"Lin, H., Hosu, V., Saupe, D.: Kadid-10k: A large-scale artificially distorted iqa database. In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, pp 1\u20133 (2019)","DOI":"10.1109\/QoMEX.2019.8743252"},{"issue":"1","key":"1702_CR30","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TIP.2015.2500021","volume":"25","author":"D Ghadiyaram","year":"2015","unstructured":"Ghadiyaram, D., Bovik, A.C.: Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process. 25(1), 372\u2013387 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"1702_CR31","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1109\/TIP.2020.2967829","volume":"29","author":"V Hosu","year":"2020","unstructured":"Hosu, V., Lin, H., Sziranyi, T., et al.: Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Trans. Image Process. 29, 4041\u20134056 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1702_CR32","doi-asserted-by":"crossref","unstructured":"Chen, C.F.R., Fan, Q., Panda, R.: Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 357\u2013366 (2021)","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"1702_CR33","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"6","key":"1702_CR34","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3390\/jimaging8060173","volume":"8","author":"D Varga","year":"2022","unstructured":"Varga, D.: No-reference quality assessment of authentically distorted images based on local and global features. J. Imag. 8(6), 173 (2022)","journal-title":"J. Imag."},{"key":"1702_CR35","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, S.: Deep learning of human visual sensitivity in image quality assessment framework. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1676\u20131684 (2017)","DOI":"10.1109\/CVPR.2017.213"},{"key":"1702_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Bai, H., Zhang, J., et\u00a0al.: Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5906\u20135916 (2023)","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"1702_CR37","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Gu, S., et\u00a0al.: Conformer: Local features coupling global representations for visual recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 367\u2013376 (2021)","DOI":"10.1109\/ICCV48922.2021.00042"},{"issue":"6","key":"1702_CR38","doi-asserted-by":"publisher","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","volume":"54","author":"M Huisman","year":"2021","unstructured":"Huisman, M., Van Rijn, J.N., Plaat, A.: A survey of deep meta-learning. Artif. Intell. Rev. 54(6), 4483\u20134541 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"1702_CR39","unstructured":"Shi, X., Chen, Z., Wang, H., et\u00a0al.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"1702_CR40","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., et\u00a0al.: Meta-learning with memory-augmented neural networks. In: International conference on machine learning, PMLR, pp 1842\u20131850 (2016)"},{"key":"1702_CR41","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., et\u00a0al.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)"},{"issue":"9","key":"1702_CR42","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., et al.: Meta-learning in neural networks: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1702_CR43","unstructured":"Finn, C., Levine, S.: Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm. arXiv preprint arXiv:1710.11622 (2017)"},{"key":"1702_CR44","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, PMLR, pp 6105\u20136114 (2019)"},{"key":"1702_CR45","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et\u00a0al.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"11","key":"1702_CR46","doi-asserted-by":"publisher","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440\u20133451 (2006)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"1702_CR47","doi-asserted-by":"publisher","first-page":"011006","DOI":"10.1117\/1.3267105","volume":"19","author":"EC Larson","year":"2010","unstructured":"Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006\u2013011006 (2010)","journal-title":"J. Electron. Imaging"},{"key":"1702_CR48","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhu, H., Zeng, Y., et\u00a0al.: Perceptual quality assessment of smartphone photography. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3677\u20133686 (2020)","DOI":"10.1109\/CVPR42600.2020.00373"},{"key":"1702_CR49","doi-asserted-by":"crossref","unstructured":"Saha, A., Mishra, S., Bovik, A.C.: Re-iqa: Unsupervised learning for image quality assessment in the wild. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5846\u20135855 (2023)","DOI":"10.1109\/CVPR52729.2023.00566"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01702-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-025-01702-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01702-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T14:47:21Z","timestamp":1757170041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-025-01702-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["1702"],"URL":"https:\/\/doi.org\/10.1007\/s00138-025-01702-y","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2025,5,15]]},"assertion":[{"value":"6 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The data involved in the paper is related to image processing and does not involve human\/animal subjects. There are no ethical and informed consent issues.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent"}}],"article-number":"80"}}