{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:18:25Z","timestamp":1783315105029,"version":"3.54.6"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["No. SJCX24 0065"],"award-info":[{"award-number":["No. SJCX24 0065"]}]},{"name":"The China Scholarship Council","award":["No. 202506090216"],"award-info":[{"award-number":["No. 202506090216"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 6522007383"],"award-info":[{"award-number":["No. 6522007383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00530-026-02274-1","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:04:49Z","timestamp":1773151489000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LEA-depth: a lightweight self-supervised monocular depth estimation with attention fusion and edge-aware distillation"],"prefix":"10.1007","volume":"32","author":[{"given":"Youchen","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"2274_CR1","doi-asserted-by":"publisher","unstructured":"Sch\u00f6n, M., Buchholz, M., Dietmayer, K.: Mgnet: Monocular geometric scene understanding for autonomous driving. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 15804\u201315815 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01551","DOI":"10.1109\/ICCV48922.2021.01551"},{"key":"2274_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112809","volume":"171","author":"X Wang","year":"2025","unstructured":"Wang, X., Zhang, Z., Li, L.: A tightly-coupled dense monocular visual-inertial odometry system with lightweight depth estimation network. Appl. Soft Comput. 171, 112809 (2025). https:\/\/doi.org\/10.1016\/j.asoc.2025.112809","journal-title":"Appl. Soft Comput."},{"key":"2274_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107619","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Zuo, S.: Self-supervised monocular depth estimation for gastrointestinal endoscopy. Comput. Methods Progr. Biomed. (2023). https:\/\/doi.org\/10.1016\/j.cmpb.2023.107619","journal-title":"Comput. Methods Progr. Biomed."},{"key":"2274_CR4","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1109\/TMM.2023.3310259","volume":"26","author":"S Shao","year":"2023","unstructured":"Shao, S., Pei, Z., Chen, W., Li, R., Liu, Z., Li, Z.: Urcdc-depth: uncertainty rectified cross-distillation with cutflip for monocular depth estimation. IEEE Trans. Multimed. 26, 3341\u20133353 (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3310259","journal-title":"IEEE Trans. Multimed."},{"key":"2274_CR5","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s00138-024-01640-1","volume":"36","author":"P Guo","year":"2025","unstructured":"Guo, P., Pan, S., Gao, W., Khoshelham, K.: Self-supervised monocular depth estimation via joint attention and intelligent mask loss. Mach. Vis. Appl. 36, 11 (2025). https:\/\/doi.org\/10.1007\/s00138-024-01640-1","journal-title":"Mach. Vis. Appl."},{"key":"2274_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3264039","volume":"72","author":"X Liu","year":"2023","unstructured":"Liu, X., Wei, W., Liu, C., Peng, Y., Huang, J., Li, J.: Real-time monocular depth estimation merging vision transformers on edge devices for aiot. IEEE Trans. Instrum. Meas. 72, 1\u20139 (2023). https:\/\/doi.org\/10.1109\/TIM.2023.3264039","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2274_CR7","doi-asserted-by":"publisher","first-page":"8470","DOI":"10.1109\/LRA.2023.3330054","volume":"8","author":"W Gao","year":"2023","unstructured":"Gao, W., Rao, D., Yang, Y., Chen, J.: Edge devices friendly self-supervised monocular depth estimation via knowledge distillation. IEEE Robot. Autom. Lett. 8, 8470\u20138477 (2023). https:\/\/doi.org\/10.1109\/LRA.2023.3330054","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2274_CR8","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/LSP.2022.3160656","volume":"29","author":"M Xiong","year":"2022","unstructured":"Xiong, M., Zhang, Z., Zhang, T., Xiong, H.: Ld-net: a lightweight network for real-time self-supervised monocular depth estimation. IEEE Signal Process. Lett. 29, 882\u2013886 (2022). https:\/\/doi.org\/10.1109\/LSP.2022.3160656","journal-title":"IEEE Signal Process. Lett."},{"key":"2274_CR9","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac\u00a0Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3828\u20133838 (2019)","DOI":"10.1109\/ICCV.2019.00393"},{"key":"2274_CR10","doi-asserted-by":"crossref","unstructured":"Watson, J., Mac\u00a0Aodha, O., Prisacariu, V., Brostow, G., Firman, M.: The temporal opportunist: Self-supervised multi-frame monocular depth. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1164\u20131174 (2021)","DOI":"10.1109\/CVPR46437.2021.00122"},{"key":"2274_CR11","first-page":"2366","volume":"27","author":"D Eigen","year":"2014","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. Adv. Neural. Inf. Process. Syst. 27, 2366\u20132374 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2274_CR12","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. arXiv:2103.13413 (2021)","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"2274_CR13","doi-asserted-by":"publisher","first-page":"7565","DOI":"10.1109\/TCSVT.2023.3275584","volume":"33","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Li, R., Shao, S., Wu, X., Chen, W.: Self-supervised monocular depth estimation with self-reference distillation and disparity offset refinement. IEEE Trans. Circuits Syst. Video Technol. 33, 7565\u20137577 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2023.3275584","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2274_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3509619","author":"S Shao","year":"2024","unstructured":"Shao, S., Pei, Z., Chen, W., Sun, D., Chen, P.C., Li, Z.: Monodiffusion: self-supervised monocular depth estimation using diffusion model. IEEE Trans. Circuits Syst. Video Technol. (2024). https:\/\/doi.org\/10.1109\/TCSVT.2024.3509619","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2274_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110026","volume":"144","author":"X Qin","year":"2025","unstructured":"Qin, X., Wang, L., Zhu, Y., Mao, F., Zhang, X., He, C., Dong, Q.: Rectified self-supervised monocular depth estimation loss for nighttime and dynamic scenes. Eng. Appl. Artif. Intell. 144, 110026 (2025). https:\/\/doi.org\/10.1016\/j.engappai.2025.110026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2274_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104862","volume":"112","author":"M Yue","year":"2022","unstructured":"Yue, M., Fu, G., Wu, M., Zhang, X., Gu, H.: Self-supervised monocular depth estimation in dynamic scenes with moving instance loss. Eng. Appl. Artif. Intell. 112, 104862 (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.104862","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2274_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, N., Nex, F., Vosselman, G., Kerle, N.: Lite-mono: A lightweight cnn and transformer architecture for self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18537\u201318546 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01778","DOI":"10.1109\/CVPR52729.2023.01778"},{"issue":"8","key":"2274_CR18","doi-asserted-by":"publisher","first-page":"474","DOI":"10.3390\/info15080474","volume":"15","author":"J Gasienica-J\u00f3zkowy","year":"2024","unstructured":"Gasienica-J\u00f3zkowy, J., Cyganek, B., Knapik, M., Glogowski, S., Przebinda, L.: Deep learning-based monocular estimation of distance and height for edge devices. Information 15(8), 474 (2024). https:\/\/doi.org\/10.3390\/info15080474","journal-title":"Information"},{"key":"2274_CR19","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/LRA.2023.3337991","volume":"9","author":"L Song","year":"2023","unstructured":"Song, L., Shi, D., Xia, J., Ouyang, Q., Qiao, Z., Jin, S., Yang, S.: Spatial-aware dynamic lightweight self-supervised monocular depth estimation. IEEE Robot. Autom. Lett. 9, 883\u2013890 (2023). https:\/\/doi.org\/10.1109\/LRA.2023.3337991","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2274_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2022.103523","volume":"85","author":"M Song","year":"2022","unstructured":"Song, M., Kim, W.: Decomposition and replacement: spatial knowledge distillation for monocular depth estimation. J. Vis. Commun. Image Represent. 85, 103523 (2022). https:\/\/doi.org\/10.1016\/j.jvcir.2022.103523","journal-title":"J. Vis. Commun. Image Represent."},{"key":"2274_CR21","doi-asserted-by":"publisher","first-page":"4492","DOI":"10.1109\/TIP.2021.3072215","volume":"30","author":"X Ye","year":"2021","unstructured":"Ye, X., Fan, X., Zhang, M., Xu, R., Zhong, W.: Unsupervised monocular depth estimation via recursive stereo distillation. IEEE Trans. Image Process. 30, 4492\u20134504 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3072215","journal-title":"IEEE Trans. Image Process."},{"key":"2274_CR22","doi-asserted-by":"publisher","first-page":"5078","DOI":"10.1109\/TCSVT.2024.3523702","volume":"35","author":"S Yu","year":"2024","unstructured":"Yu, S., Wu, M., Lam, S.-K.: Vfm-depth: leveraging vision foundation model for self-supervised monocular depth estimation. IEEE Trans. Circuits Syst. Video Technol. 35, 5078\u20135091 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2024.3523702","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2274_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130314","volume":"640","author":"C Yang","year":"2025","unstructured":"Yang, C., Lu, Y., Qiu, Y., Wang, Y.: Ikfd-depth: enhancing self-supervised monocular depth estimation with incremental knowledge distillation and feature decoupling self-distillation. Neurocomputing 640, 130314 (2025). https:\/\/doi.org\/10.1016\/j.neucom.2025.130314","journal-title":"Neurocomputing"},{"key":"2274_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., Wang, C.: Rethinking mobile block for efficient attention-based models. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1389\u20131400 (2023)","DOI":"10.1109\/ICCV51070.2023.00134"},{"key":"2274_CR25","doi-asserted-by":"publisher","first-page":"8883","DOI":"10.1109\/TPAMI.2024.3411571","volume":"46","author":"S Shao","year":"2024","unstructured":"Shao, S., Pei, Z., Chen, W., Chen, P.C., Li, Z.: Nddepth: normal-distance assisted monocular depth estimation and completion. IEEE Trans. Pattern Anal. Mach. Intell. 46, 8883\u20138899 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3411571","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2274_CR26","doi-asserted-by":"publisher","first-page":"5170","DOI":"10.1109\/TPAMI.2021.3074363","volume":"44","author":"Z Qin","year":"2021","unstructured":"Qin, Z., Wang, J., Lu, Y.: Monogrnet: a general framework for monocular 3d object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5170\u20135184 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3074363","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2274_CR27","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1109\/TPAMI.2018.2839602","volume":"41","author":"E Ricci","year":"2018","unstructured":"Ricci, E., Ouyang, W., Wang, X., Sebe, N.: Monocular depth estimation using multi-scale continuous crfs as sequential deep networks. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1426\u20131440 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2018.2839602","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2274_CR28","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1109\/TIP.2025.3533207","volume":"34","author":"C Feng","year":"2025","unstructured":"Feng, C., Zhang, C., Chen, Z., Hu, W., Lu, K., Ge, L.: Self-supervised monocular depth estimation with dual-path encoders and offset field interpolation. IEEE Trans. Image Process. 34, 939\u2013954 (2025). https:\/\/doi.org\/10.1109\/TIP.2025.3533207","journal-title":"IEEE Trans. Image Process."},{"key":"2274_CR29","doi-asserted-by":"publisher","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1851\u20131858 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.700","DOI":"10.1109\/CVPR.2017.700"},{"key":"2274_CR30","doi-asserted-by":"publisher","first-page":"13706","DOI":"10.1109\/TITS.2024.3402655","volume":"25","author":"J Wei","year":"2024","unstructured":"Wei, J., Pan, S., Gao, W., Guo, P.: Lam-depth: Laplace-attention module-based self-supervised monocular depth estimation. IEEE Trans. Intell. Transp. Syst. 25, 13706\u201313716 (2024). https:\/\/doi.org\/10.1109\/TITS.2024.3402655","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"2274_CR31","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s00530-025-01700-0","volume":"31","author":"Z Lu","year":"2025","unstructured":"Lu, Z., Chen, Y.: Self-supervised monocular depth estimation via multiple bilateral consistency. Multimed. Syst. 31(2), 111 (2025). https:\/\/doi.org\/10.1007\/s00530-025-01700-0","journal-title":"Multimed. Syst."},{"key":"2274_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2025.104413","volume":"108","author":"X Xiang","year":"2025","unstructured":"Xiang, X., Wang, Y., Li, X., Zhang, L., Zhen, X.: Self-supervised monocular depth estimation with large kernel attention and dynamic scene perception. J. Vis. Commun. Image Represent. 108, 104413 (2025). https:\/\/doi.org\/10.1016\/j.jvcir.2025.104413","journal-title":"J. Vis. Commun. Image Represent."},{"key":"2274_CR33","unstructured":"Grigore, I., Popa, C.-A.: Mambadepth: enhancing long-range dependency for self-supervised fine-structured monocular depth estimation. arXiv:2406.04532 (2024)"},{"issue":"1","key":"2274_CR34","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s00530-024-01605-4","volume":"31","author":"H Li","year":"2025","unstructured":"Li, H., Zhang, Z., Hao, Z., Xu, B., Wang, W., Sun, J.: Par-mono: monocular video depth estimation network based on channel separation and dynamic attention. Multimed. Syst. 31(1), 6 (2025). https:\/\/doi.org\/10.1007\/s00530-024-01605-4","journal-title":"Multimed. Syst."},{"key":"2274_CR35","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s11760-024-03720-1","volume":"19","author":"I Ardiyanto","year":"2025","unstructured":"Ardiyanto, I., Al-Fahsi, R.D.H.: Lightweight monocular depth estimation network for robotics using intercept block ghostnet. SIViP 19, 34 (2025). https:\/\/doi.org\/10.1007\/s11760-024-03720-1","journal-title":"SIViP"},{"key":"2274_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, X., Shi, M., Xian, K., Cao, Z.: Knowledge distillation for fast and accurate monocular depth estimation on mobile devices. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2457\u20132465 (2021)","DOI":"10.1109\/CVPRW53098.2021.00278"},{"key":"2274_CR37","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.patrec.2023.11.001","volume":"176","author":"S Lee","year":"2023","unstructured":"Lee, S., Im, W., Yoon, S.-E.: Multi-resolution distillation for self-supervised monocular depth estimation. Pattern Recogn. Lett. 176, 215\u2013222 (2023). https:\/\/doi.org\/10.1016\/j.patrec.2023.11.001","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"2274_CR38","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1109\/TCSVT.2024.3492201","volume":"35","author":"X Ye","year":"2024","unstructured":"Ye, X., Ou, Y., Wu, B., Xu, R., Li, H.: Self-supervised monocular depth estimation from videos via adaptive reconstruction constraints. IEEE Trans. Circuits Syst. Video Technol. 35(3), 2161\u20132172 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2024.3492201","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2274_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105110","volume":"148","author":"S Choi","year":"2024","unstructured":"Choi, S., Choi, D., Kim, D.: Tie-kd: teacher-independent and explainable knowledge distillation for monocular depth estimation. Image Vis. Comput. 148, 105110 (2024). https:\/\/doi.org\/10.1016\/j.imavis.2024.105110","journal-title":"Image Vis. Comput."},{"key":"2274_CR40","doi-asserted-by":"crossref","unstructured":"Chen, R., Luo, H., Zhao, F., Yu, J., Jia, Y., Wang, J., Ma, X.: Structure-centric robust monocular depth estimation via knowledge distillation. In: Proceedings of the Asian Conference on Computer Vision, pp. 2970\u20132987 (2024)","DOI":"10.1007\/978-981-96-0969-7_8"},{"key":"2274_CR41","unstructured":"He, X., Guo, D., Li, H., Li, R., Cui, Y., Zhang, C.: Distill any depth: distillation creates a stronger monocular depth estimator. arXiv:2502.19204 (2025)"},{"key":"2274_CR42","doi-asserted-by":"publisher","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: Conet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 603\u2013612 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00069","DOI":"10.1109\/ICCV.2019.00069"},{"key":"2274_CR43","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11534\u201311542 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2274_CR44","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Fan, X., Shi, P., Xin, Y.: R-msfm: recurrent multi-scale feature modulation for monocular depth estimating. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 12777\u201312786 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01254","DOI":"10.1109\/ICCV48922.2021.01254"},{"key":"2274_CR45","doi-asserted-by":"publisher","unstructured":"Lyu, X., Liu, L., Wang, M., Kong, X., Liu, L., Liu, Y., Chen, X., Yuan, Y.: Hr-depth: high resolution self-supervised monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2294\u20132301 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i3.16329","DOI":"10.1609\/aaai.v35i3.16329"},{"key":"2274_CR46","doi-asserted-by":"publisher","unstructured":"Yan, J., Zhao, H., Bu, P., Jin, Y.: Channel-wise attention-based network for self-supervised monocular depth estimation. In: 2021 International Conference on 3D Vision (3DV), pp. 464\u2013473 (2021). https:\/\/doi.org\/10.1109\/3DV53792.2021.00056","DOI":"10.1109\/3DV53792.2021.00056"},{"key":"2274_CR47","doi-asserted-by":"publisher","unstructured":"Bae, J., Moon, S., Im, S.: Deep digging into the generalization of self-supervised monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 187\u2013196 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i1.25090","DOI":"10.1609\/aaai.v37i1.25090"},{"issue":"1","key":"2274_CR48","doi-asserted-by":"publisher","first-page":"49","DOI":"10.63496\/ejcs.Vol1.Iss1.16","volume":"1","author":"S Al Rawahi","year":"2025","unstructured":"Al Rawahi, S.: A comparison of sobel and prewitt edge detection operators. East J. Comput. Sci. 1(1), 49\u201358 (2025). https:\/\/doi.org\/10.63496\/ejcs.Vol1.Iss1.16","journal-title":"East J. Comput. Sci."},{"key":"2274_CR49","doi-asserted-by":"publisher","DOI":"10.3389\/frsip.2022.826967","volume":"2","author":"R Sun","year":"2022","unstructured":"Sun, R., Lei, T., Chen, Q., Wang, Z., Du, X., Zhao, W., Nandi, A.K.: Survey of image edge detection. Front. Signal Process. 2, 826967 (2022). https:\/\/doi.org\/10.3389\/frsip.2022.826967","journal-title":"Front. Signal Process."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-026-02274-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-026-02274-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-026-02274-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:08:17Z","timestamp":1783314497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-026-02274-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2274"],"URL":"https:\/\/doi.org\/10.1007\/s00530-026-02274-1","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,10]]},"assertion":[{"value":"27 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"218"}}