{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:51:23Z","timestamp":1790103083673,"version":"4.0.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61963006"],"award-info":[{"award-number":["61963006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Science and Technology Projects in Guangxi","award":["Gui Ke AA22068064"],"award-info":[{"award-number":["Gui Ke AA22068064"]}]},{"name":"the Industry-University-Research Innovation Fund for Chinese Universities","award":["2021ZYA08002"],"award-info":[{"award-number":["2021ZYA08002"]}]},{"DOI":"10.13039\/501100009329","name":"Guangxi Science and Technology Planning Project","doi-asserted-by":"crossref","award":["Gui Ke AD21220161"],"award-info":[{"award-number":["Gui Ke AD21220161"]}],"id":[{"id":"10.13039\/501100009329","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangxi Key Research and Development Project","award":["Gui Ke AB23075164"],"award-info":[{"award-number":["Gui Ke AB23075164"]}]},{"name":"the Basic Ability Improvement Project for Young and Middle-Aged University Teachers of Guangxi","award":["2021KY0364"],"award-info":[{"award-number":["2021KY0364"]}]},{"name":"the Doctoral Fund Project of Guangxi University of Science and Technology","award":["Xiao Ke Bo 23Z08"],"award-info":[{"award-number":["Xiao Ke Bo 23Z08"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03549-2","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T18:17:23Z","timestamp":1721845043000},"page":"2511-2527","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["EDM: a enhanced diffusion models for image restoration in complex scenes"],"prefix":"10.1007","volume":"41","author":[{"given":"JiaYan","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuanSheng","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JunYi","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"3549_CR1","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: deblurring (orders-of-magnitude) faster and better. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8878\u20138887 (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"3549_CR2","doi-asserted-by":"crossref","unstructured":"Suin, M., Purohit, K., Rajagopalan, A.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3606\u20133615 (2020)","DOI":"10.1109\/CVPR42600.2020.00366"},{"key":"3549_CR3","doi-asserted-by":"crossref","unstructured":"Yeh, R.A., Chen, C., Yian\u00a0Lim, T., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5485\u20135493 (2017)","DOI":"10.1109\/CVPR.2017.728"},{"key":"3549_CR4","doi-asserted-by":"crossref","unstructured":"Lee, W., Son, S., Lee, K.M.: Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17725\u201317734 (2022)","DOI":"10.1109\/CVPR52688.2022.01720"},{"key":"3549_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep cnn denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"key":"3549_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ghanem, B.: Ista-net: Interpretable optimization-inspired deep network for image compressive sensing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1828\u20131837 (2018)","DOI":"10.1109\/CVPR.2018.00196"},{"issue":"4","key":"3549_CR7","first-page":"1351","volume":"39","author":"X Wang","year":"2023","unstructured":"Wang, X., Wu, K., Zhang, Y., Xiao, Y., Xu, P.: A GAN-based denoising method for Chinese stele and rubbing calligraphic image. Vis. Comput. 39(4), 1351\u20131362 (2023)","journal-title":"Vis. Comput."},{"key":"3549_CR8","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change\u00a0Loy, C.: Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"3549_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103776","volume":"91","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Xia, R., Zou, K., Yang, K.: Ffti: image inpainting algorithm via features fusion and two-steps inpainting. J. Vis. Commun. Image Represent. 91, 103776 (2023)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3549_CR10","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"3549_CR11","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents, 2022, vol. 7. arXiv:2204.06125 (2022)"},{"key":"3549_CR12","unstructured":"Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S.K.S., Ayan, B.K., Mahdavi, S.S., Lopes, R.G., et al.: Photorealistic text-to-image diffusion models with deep language understanding, arXiv: 2205.11487 (2022)"},{"key":"3549_CR13","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van\u00a0Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11461\u201311471 (2022)","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"3549_CR14","unstructured":"Nair, N.G., Mei, K., Patel, V.M.: At-ddpm: Restoring faces degraded by atmospheric turbulence using denoising diffusion probabilistic models. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3434\u20133443 (2023)"},{"key":"3549_CR15","unstructured":"Yang, T., Ren, P., Zhang, L., et\u00a0al.: Synthesizing realistic image restoration training pairs: A diffusion approach. arXiv preprint arXiv:2303.06994 (2023)"},{"key":"3549_CR16","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: Eapt: efficient attention pyramid transformer for image processing. IEEE Trans. Multimedia 25, 50\u201361 (2021)","journal-title":"IEEE Trans. Multimedia"},{"issue":"8","key":"3549_CR17","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1109\/TNNLS.2021.3116209","volume":"34","author":"Z Xie","year":"2021","unstructured":"Xie, Z., Zhang, W., Sheng, B., Li, P., Chen, C.P.: Bagfn: broad attentive graph fusion network for high-order feature interactions. IEEE Trans. Neural Netw. Learn. Syst. 34(8), 4499\u20134513 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3549_CR18","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: Places2: A large-scale database for scene understanding. http:\/\/places2.csail.mit.edu (2015)"},{"key":"3549_CR19","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., Yan, S.: Deep joint rain detection and removal from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1357\u20131366 (2017)","DOI":"10.1109\/CVPR.2017.183"},{"issue":"2","key":"3549_CR20","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1109\/TVCG.2018.2869326","volume":"26","author":"B Sheng","year":"2018","unstructured":"Sheng, B., Li, P., Jin, Y., Tan, P., Lee, T.-Y.: Intrinsic image decomposition with step and drift shading separation. IEEE Trans. Visual Comput. Graphics 26(2), 1332\u20131346 (2018)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"issue":"5","key":"3549_CR21","doi-asserted-by":"publisher","first-page":"2735","DOI":"10.1109\/TCYB.2019.2934823","volume":"51","author":"H Guo","year":"2019","unstructured":"Guo, H., Sheng, B., Li, P., Chen, C.P.: Multiview high dynamic range image synthesis using fuzzy broad learning system. IEEE Trans. Cybern. 51(5), 2735\u20132747 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"3549_CR22","doi-asserted-by":"crossref","unstructured":"Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 417\u2013424 (2000)","DOI":"10.1145\/344779.344972"},{"issue":"11","key":"3549_CR23","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1016\/j.imavis.2008.02.010","volume":"26","author":"S Bettahar","year":"2008","unstructured":"Bettahar, S., Stambouli, A.B.: Shock filter coupled to curvature diffusion for image denoising and sharpening. Image Vis. Comput. 26(11), 1481\u20131489 (2008)","journal-title":"Image Vis. Comput."},{"issue":"6","key":"3549_CR24","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.imavis.2009.11.008","volume":"28","author":"S Binczak","year":"2010","unstructured":"Binczak, S., Sliwa, T., Jacquir, S., Bilbault, J.-M.: Reaction-diffusion network for geometric multiscale high speed image processing. Image Vis. Comput. 28(6), 914\u2013926 (2010)","journal-title":"Image Vis. Comput."},{"key":"3549_CR25","doi-asserted-by":"crossref","unstructured":"Dong, C., Deng, Y., Loy, C.C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576\u2013584 (2015)","DOI":"10.1109\/ICCV.2015.73"},{"issue":"2","key":"3549_CR26","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3549_CR27","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Free-form image inpainting with gated convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4471\u20134480 (2019)","DOI":"10.1109\/ICCV.2019.00457"},{"issue":"13","key":"3549_CR28","doi-asserted-by":"publisher","first-page":"14693","DOI":"10.1007\/s10489-021-03092-w","volume":"52","author":"J Huang","year":"2022","unstructured":"Huang, J., Ding, W., Lv, J., Yang, J., Dong, H., Del Ser, J., Xia, J., Ren, T., Wong, S.T., Yang, G.: Edge-enhanced dual discriminator generative adversarial network for fast mri with parallel imaging using multi-view information. Appl. Intell. 52(13), 14693\u201314710 (2022)","journal-title":"Appl. Intell."},{"key":"3549_CR29","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Fu, J., Chao, H., Guo, B.: Aggregated contextual transformations for high-resolution image inpainting. IEEE Trans. Vis. Comput. 29, 3266\u20133280 (2022)","DOI":"10.1109\/TVCG.2022.3156949"},{"key":"3549_CR30","unstructured":"Farnia, F., Ozdaglar, A.: Do gans always have nash equilibria? In: International Conference on Machine Learning, pp. 3029\u20133039. PMLR (2020)"},{"key":"3549_CR31","unstructured":"Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988 (2022)"},{"key":"3549_CR32","doi-asserted-by":"crossref","unstructured":"Sutedy, M.F., Qomariyah, N.N.: Text to image latent diffusion model with dreambooth fine tuning for automobile image generation. In: 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 440\u2013445. IEEE (2022)","DOI":"10.1109\/ISRITI56927.2022.10052908"},{"key":"3549_CR33","unstructured":"Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)"},{"key":"3549_CR34","doi-asserted-by":"crossref","unstructured":"Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., Van\u00a0Gool, L.: Diffir: efficient diffusion model for image restoration. arXiv preprint arXiv:2303.09472 (2023)","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"3549_CR35","unstructured":"Wang, Y., Yu, J., Zhang, J.: Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490 (2022)"},{"key":"3549_CR36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3549_CR37","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"3549_CR38","unstructured":"Cordonnier, J.-B., Loukas, A., Jaggi, M.: Multi-head attention: Collaborate instead of concatenate. arXiv preprint arXiv:2006.16362 (2020)"},{"key":"3549_CR39","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"3549_CR40","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"3549_CR41","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"3549_CR42","doi-asserted-by":"crossref","unstructured":"Lin, H., Cheng, X., Wu, X., Shen, D.: Cat: Cross attention in vision transformer. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICME52920.2022.9859720"},{"key":"3549_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","volume":"107","author":"S Elfwing","year":"2018","unstructured":"Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3\u201311 (2018)","journal-title":"Neural Netw."},{"key":"3549_CR44","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)"},{"key":"3549_CR45","unstructured":"Misra, D.: Mish: A self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681 (2019)"},{"key":"3549_CR46","unstructured":"Nwankpa, C., Ijomah, W., Gachagan, A., Marshall, S.: Activation functions: comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378 (2018)"},{"key":"3549_CR47","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Orr, G.B., M\u00fcller, K.-R.: Efficient backprop. In: Neural Networks: Tricks of the Trade, pp. 9\u201350 (2002)","DOI":"10.1007\/3-540-49430-8_2"},{"key":"3549_CR48","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807\u2013814 (2010)"},{"key":"3549_CR49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3549_CR50","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference on Pattern Recognition, pp. 2366\u20132369. IEEE (2010)","DOI":"10.1109\/ICPR.2010.579"},{"key":"3549_CR51","unstructured":"Varma, R.: Picking loss functions-a comparison between MSE, cross entropy, and hinge loss. rohanvarma. me, Jan (2018)"},{"key":"3549_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"5","key":"3549_CR53","first-page":"2567","volume":"44","author":"K Ding","year":"2020","unstructured":"Ding, K., Ma, K., Wang, S., Simoncelli, E.P.: Image quality assessment: unifying structure and texture similarity. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2567\u20132581 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3549_CR54","doi-asserted-by":"crossref","unstructured":"Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: Mat: Mask-aware transformer for large hole image inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10758\u201310768 (2022)","DOI":"10.1109\/CVPR52688.2022.01049"},{"key":"3549_CR55","doi-asserted-by":"crossref","unstructured":"Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., Lempitsky, V.: Resolution-robust large mask inpainting with fourier convolutions. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2149\u20132159 (2022)","DOI":"10.1109\/WACV51458.2022.00323"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03549-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03549-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03549-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:32:13Z","timestamp":1741001533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03549-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3549"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03549-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"11 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}