{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:03:04Z","timestamp":1784566984184,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No.2023YFB4502804"],"award-info":[{"award-number":["No.2023YFB4502804"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["No. 62025603"],"award-info":[{"award-number":["No. 62025603"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U22B2051"],"award-info":[{"award-number":["No. U22B2051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62302411"],"award-info":[{"award-number":["No. 62302411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["No. 2023M732948"],"award-info":[{"award-number":["No. 2023M732948"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11263-026-02832-0","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:27:31Z","timestamp":1779301651000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boosting Filter Optimization and Prompt-Guided Decoding for Mixed Degradation Image Restoration"],"prefix":"10.1007","volume":"134","author":[{"given":"Yubin","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3912-9306","authenticated-orcid":false,"given":"Xiaoshuai","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"2832_CR1","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., & Brown, M.S. (2018). A high-quality denoising dataset for smartphone cameras. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1692\u20131700","DOI":"10.1109\/CVPR.2018.00182"},{"issue":"2","key":"2832_CR2","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1007\/s11263-024-02207-3","volume":"133","author":"MA Butt","year":"2025","unstructured":"Butt, M. A., Ali, H., Qayyum, A., Sultani, W., Al-Fuqaha, A., & Qadir, J. (2025). R 2 s100k: Road-region segmentation dataset for semi-supervised autonomous driving in the wild. Int. J. Comput. Vis., 133(2), 910\u2013928.","journal-title":"Int. J. Comput. Vis."},{"key":"2832_CR3","unstructured":"Cao, J., Cao, Y., Pang, L., Meng, D., & Cao, X. (2024). HAIR: Hypernetworks-based All-in-One Image Restoration . https:\/\/arxiv.org\/abs\/2408.08091"},{"key":"2832_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L., Chu, X., Zhang, X., & Sun, J. (2022). Simple baselines for image restoration. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 17\u201333 . Springer","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"2832_CR5","doi-asserted-by":"crossref","unstructured":"Chen, W.-T., Fang, H.-Y., Hsieh, C.-L., Tsai, C.-C., Chen, I., Ding, J.-J., & Kuo, S.-Y., et al. (2021). All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In: Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), pp. 4196\u20134205","DOI":"10.1109\/ICCV48922.2021.00416"},{"key":"2832_CR6","volume-title":"Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention","author":"Z Chen","year":"2024","unstructured":"Chen, Z., He, Z., & Lu, Z.-M. (2024). Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention. Image Process: IEEE Trans."},{"key":"2832_CR7","volume-title":"Image restoration via frequency selection","author":"Y Cui","year":"2023","unstructured":"Cui, Y., Ren, W., Cao, X., & Knoll, A. (2023). Image restoration via frequency selection. Intell: IEEE Trans. Pattern Anal. Mach."},{"key":"2832_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3419007","volume-title":"Revitalizing convolutional network for image restoration","author":"Y Cui","year":"2024","unstructured":"Cui, Y., Ren, W., Cao, X., & Knoll, A. (2024). Revitalizing convolutional network for image restoration. Intell: IEEE Trans. Pattern Anal. Mach."},{"key":"2832_CR9","first-page":"1426","volume":"38","author":"Y Cui","year":"2024","unstructured":"Cui, Y., Ren, W., & Knoll, A. (2024). Omni-kernel network for image restoration. In: Proc. AAAI Conf. Artif. Intell., 38, 1426\u20131434.","journal-title":"In: Proc. AAAI Conf. Artif. Intell."},{"key":"2832_CR10","doi-asserted-by":"crossref","unstructured":"Chen, S., Ye, T., Lin, Y., Jin, Y., Yang, Y., Chen, H., Lai, J., Fei, S., Xing, Z., & Tsung, F., et al. (2025). Genhaze: Pioneering controllable one-step realistic haze generation for real-world dehazing. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 9194\u20139205","DOI":"10.1109\/ICCV51701.2025.00859"},{"key":"2832_CR11","unstructured":"Chen, S., Ye, T., Liu, Y., & Chen, E. (2022). Snowformer: Context interaction transformer with scale-awareness for single image desnowing. arXiv preprint arXiv:2208.09703"},{"key":"2832_CR12","doi-asserted-by":"crossref","unstructured":"Chen, S., Ye, T., Xue, C., Chen, H., Liu, Y., Chen, E., & Zhu, L. (2023). Uncertainty-driven dynamic degradation perceiving and background modeling for efficient single image desnowing. In: Proc. 31st ACM Int. Conf. Multimedia, pp. 4269\u20134280","DOI":"10.1145\/3581783.3612003"},{"key":"2832_CR13","doi-asserted-by":"crossref","unstructured":"Flepp, R., Ignatov, A., Timofte, R., & Van\u00a0Gool, L. (2024). Real-world mobile image denoising dataset with efficient baselines. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 22368\u201322377","DOI":"10.1109\/CVPR52733.2024.02111"},{"key":"2832_CR14","unstructured":"Fei, S., Ye, T., Wang, L., & Zhu, L. (2025). Lucidflux: Caption-free universal image restoration via a large-scale diffusion transformer. arXiv preprint arXiv:2509.22414"},{"key":"2832_CR15","first-page":"2960","volume":"39","author":"H Feng","year":"2025","unstructured":"Feng, H., Zhou, H., Ye, T., Chen, S., & Zhu, L. (2025). Residual diffusion deblurring model for single image defocus deblurring. Proc. AAAI Conf. Artif. Intell., 39, 2960\u20132968.","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2832_CR16","doi-asserted-by":"crossref","unstructured":"Guo, Y., Gao, Y., Lu, Y., Zhu, H., Liu, R.W., & He, S. (2024). Onerestore: A universal restoration framework for composite degradation. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 255\u2013272 . Springer","DOI":"10.1007\/978-3-031-72655-2_15"},{"key":"2832_CR17","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., & Xia, S.-T. (2025). Mambair: A simple baseline for image restoration with state-space model. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 222\u2013241 . Springer","DOI":"10.1007\/978-3-031-72649-1_13"},{"key":"2832_CR18","unstructured":"Gao, H., Lei, X., Zhang, Y., Xu, X., Jiang, G., & Ma, L. (2025). Learning to restore multi-degraded images via ingredient decoupling and task-aware path adaptation. arXiv preprint arXiv:2511.04920"},{"key":"2832_CR19","doi-asserted-by":"crossref","unstructured":"Gu, Y., Meng, Y., Chen, S., Ji, J., Sun, X., Ruan, W., & Ji, R. (2025). Sfir: Optimizing spatial and frequency domains for image restoration. Pattern Recognit., 112188","DOI":"10.1016\/j.patcog.2025.112188"},{"key":"2832_CR20","doi-asserted-by":"crossref","unstructured":"Gu, Y., Meng, Y., Ji, J., & Sun, X. (2025). Acl: Activating capability of linear attention for image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 17913\u201317923","DOI":"10.1109\/CVPR52734.2025.01669"},{"key":"2832_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3436651","volume-title":"Linear combinations of patches are unreasonably effective for single-image denoising","author":"S Herbreteau","year":"2024","unstructured":"Herbreteau, S., & Kervrann, C. (2024). Linear combinations of patches are unreasonably effective for single-image denoising. Image Process: IEEE Trans."},{"key":"2832_CR22","first-page":"12892","volume":"38","author":"K Jiang","year":"2024","unstructured":"Jiang, K., Jiang, J., Liu, X., Xu, X., & Ma, X. (2024). Fmrnet: Image deraining via frequency mutual revision. Proc. AAAI Conf. Artif. Intell., 38, 12892\u201312900.","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2832_CR23","doi-asserted-by":"crossref","unstructured":"Lai, J., Chen, S., Lin, Y., Ye, T., Liu, Y., Fei, S., Xing, Z., Wu, H., Wang, W., & Zhu, L. (2025). Snowmaster: Comprehensive real-world image desnowing via mllm with multi-model feedback optimization. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4302\u20134312","DOI":"10.1109\/CVPR52734.2025.00406"},{"key":"2832_CR24","unstructured":"Luo, Z., Gustafsson, F.K., Zhao, Z., Sj\u00f6lund, J., & Sch\u00f6n, T.B. (2023). Controlling vision-language models for multi-task image restoration. arXiv preprint arXiv:2310.01018"},{"key":"2832_CR25","first-page":"3378","volume":"38","author":"B Lin","year":"2024","unstructured":"Lin, B., Jin, Y., Yan, W., Ye, W., Yuan, Y., Zhang, S., & Tan, R. T. (2024). Nightrain: Nighttime video deraining via adaptive-rain-removal and adaptive-correction. Proc. AAAI Conf. Artif. Intell., 38, 3378\u20133385.","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2832_CR26","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., & Peng, X. (2022). All-in-one image restoration for unknown corruption. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 17452\u201317462","DOI":"10.1109\/CVPR52688.2022.01693"},{"key":"2832_CR27","doi-asserted-by":"crossref","unstructured":"Li, F., Shen, L., Mi, Y., & Li, Z. (2022). Drcnet: Dynamic image restoration contrastive network. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 514\u2013532 . Springer","DOI":"10.1007\/978-3-031-19800-7_30"},{"key":"2832_CR28","first-page":"5307","volume":"39","author":"Y Lin","year":"2025","unstructured":"Lin, Y., Ye, T., Chen, S., Fu, Z., Wang, Y., Chai, W., Xing, Z., Li, W., Zhu, L., & Ding, X. (2025). Aglldiff: Guiding diffusion models towards unsupervised training-free real-world low-light image enhancement. Proc. AAAI Conf. Artif. Intell., 39, 5307\u20135315.","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2832_CR29","doi-asserted-by":"crossref","unstructured":"Li, B., Zhao, H., Wang, W., Hu, P., Gou, Y., & Peng, X. (2025). Mair: A locality-and continuity-preserving mamba for image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 7491\u20137501","DOI":"10.1109\/CVPR52734.2025.00702"},{"key":"2832_CR30","doi-asserted-by":"crossref","unstructured":"Meng, Y., Gu, Y., Sun, X., Ji, J., Ruan, W., & Ji, R. (2025). Wavelet-based learning and optimized sampling for image deraining. Pattern Recognit., 112782","DOI":"10.1016\/j.patcog.2025.112782"},{"key":"2832_CR31","doi-asserted-by":"crossref","unstructured":"Mou, C., Wang, Q., & Zhang, J. (2022). Deep generalized unfolding networks for image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 17399\u201317410","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"2832_CR32","unstructured":"Mao, J., Yang, Y., Yin, X., Shao, L., & Tang, H. (2024). Allrestorer: All-in-one transformer for image restoration under composite degradations. arXiv preprint arXiv:2411.10708"},{"key":"2832_CR33","doi-asserted-by":"crossref","unstructured":"Potlapalli, V., Zamir, S.W., Khan, S.H., & Shahbaz\u00a0Khan, F. (2024). Promptir: Prompting for all-in-one image restoration. Adv. Neural Inf. Process. Syst. (NeurIPS) 36","DOI":"10.52202\/075280-3121"},{"key":"2832_CR34","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2832_CR35","unstructured":"Vaswani, A. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"2832_CR36","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Yasarla, R., & Patel, V.M. (2022). Transweather: Transformer-based restoration of images degraded by adverse weather conditions. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2353\u20132363","DOI":"10.1109\/CVPR52688.2022.00239"},{"key":"2832_CR37","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/TIP.2023.3334556","volume":"33","author":"Q Wang","year":"2023","unstructured":"Wang, Q., Jiang, K., Wang, Z., Ren, W., Zhang, J., & Lin, C.-W. (2023). Multi-scale fusion and decomposition network for single image deraining. IEEE Trans. Image Process., 33, 191\u2013204.","journal-title":"IEEE Trans. Image Process."},{"key":"2832_CR38","doi-asserted-by":"publisher","first-page":"8086","DOI":"10.1109\/TIP.2025.3638662","volume":"34","author":"Y Wang","year":"2025","unstructured":"Wang, Y., Li, Y., Zheng, Z., Zhang, X.-P., & Wei, M. (2025). M2restore: Mixture-of-experts-based mamba-cnn fusion framework for all-in-one image restoration. IEEE Trans. Image Process., 34, 8086\u20138100. https:\/\/doi.org\/10.1109\/TIP.2025.3638662","journal-title":"IEEE Trans. Image Process."},{"key":"2832_CR39","doi-asserted-by":"crossref","unstructured":"Ye, T., Chen, S., Bai, J., Shi, J., Xue, C., Jiang, J., Yin, J., Chen, E., & Liu, Y. (2023). Adverse weather removal with codebook priors. In: Proc. IEEE\/CVF Int. Conf. Comput. Vis., pp. 12653\u201312664","DOI":"10.1109\/ICCV51070.2023.01163"},{"key":"2832_CR40","doi-asserted-by":"crossref","unstructured":"Ye, T., Chen, S., Chai, W., Xing, Z., Qin, J., Lin, G., & Zhu, L. (2024). Learning diffusion texture priors for image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2524\u20132534","DOI":"10.1109\/CVPR52733.2024.00244"},{"key":"2832_CR41","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., & Yan, S. (2017). Deep joint rain detection and removal from a single image. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1357\u20131366","DOI":"10.1109\/CVPR.2017.183"},{"key":"2832_CR42","doi-asserted-by":"crossref","unstructured":"Yao, M., Xu, R., Guan, Y., Huang, J., & Xiong, Z. (2024). Neural degradation representation learning for all-in-one image restoration. Image Process: IEEE Trans.","DOI":"10.1109\/TIP.2024.3456583"},{"issue":"9","key":"2832_CR43","first-page":"5199","volume":"44","author":"Z-B Yu","year":"2021","unstructured":"Yu, Z.-B., & Zhang, M.-L. (2021). Multi-label classification with label-specific feature generation: A wrapped approach. IEEE Trans. Pattern Anal. Mach. Intell., 44(9), 5199\u20135210.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2832_CR44","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., & Shao, L. (2021). Multi-stage progressive image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 14821\u201314831","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"2832_CR45","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., & Yang, M.-H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 5728\u20135739","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"2832_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, H., Ba, Y., Yang, E., Mehra, V., Gella, B., Suzuki, A., Pfahnl, A., Chandrappa, C.C., Wong, A., & Kadambi, A. (2023). Weatherstream: Light transport automation of single image deweathering. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 13499\u201313509","DOI":"10.1109\/CVPR52729.2023.01297"},{"key":"2832_CR47","doi-asserted-by":"crossref","unstructured":"Zhou, S., Chen, D., Pan, J., Shi, J., & Yang, J. (2024). Adapt or perish: Adaptive sparse transformer with attentive feature refinement for image restoration. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2952\u20132963","DOI":"10.1109\/CVPR52733.2024.00285"},{"key":"2832_CR48","first-page":"7873","volume":"38","author":"W Zou","year":"2024","unstructured":"Zou, W., Gao, H., Ye, T., Chen, L., Yang, W., Huang, S., Chen, H., & Chen, S. (2024). Vqcnir: clearer night image restoration with vector-quantized codebook. Proc. AAAI Conf. Artif. Intell., 38, 7873\u20137881.","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"7","key":"2832_CR49","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1109\/TVCG.2022.3233900","volume":"30","author":"D Zhao","year":"2024","unstructured":"Zhao, D., Li, J., Li, H., & Xu, L. (2024). Stripe sensitive convolution for omnidirectional image dehazing. IEEE Trans. Vis. Comput. Graph., 30(7), 3516\u20133531.","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"2832_CR50","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, T., Fu, X., Yang, X., Guo, X., Dai, J., Qiao, Y., & Hu, X. (2023). Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 21747\u201321758","DOI":"10.1109\/CVPR52729.2023.02083"},{"issue":"12","key":"2832_CR51","doi-asserted-by":"publisher","first-page":"5582","DOI":"10.1007\/s11263-024-02141-4","volume":"132","author":"Z Zheng","year":"2024","unstructured":"Zheng, Z., Zhong, Y., Ma, A., & Zhang, L. (2024). Single-temporal supervised learning for universal remote sensing change detection. Int. J. Comput. Vis., 132(12), 5582\u20135602.","journal-title":"Int. J. Comput. Vis."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02832-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02832-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02832-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:13:56Z","timestamp":1784564036000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02832-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":51,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2832"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02832-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]},"assertion":[{"value":"21 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":1,"name":"Ethics","label":"Conflict of interest\/Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant ethics approval to disclose.","order":2,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors agreed to participate in this work and made clear contributions.","order":3,"name":"Ethics","label":"Consent for participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute\/organization where the work has been carried out.","order":4,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Our code is publicly released at\n                      \n                      .","order":5,"name":"Ethics","label":"Code availability","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"280"}}