{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T19:41:33Z","timestamp":1791142893425,"version":"4.1.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00371-023-02912-z","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T02:02:00Z","timestamp":1688522520000},"page":"2219-2233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MEStereo-Du2CNN: a dual-channel CNN for learning robust depth estimates from multi-exposure stereo images for HDR 3D applications"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3354-666X","authenticated-orcid":false,"given":"Rohit","family":"Choudhary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3243-3321","authenticated-orcid":false,"given":"Mansi","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1225-2999","authenticated-orcid":false,"given":"T. V.","family":"Uma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4596-0000","authenticated-orcid":false,"given":"Rithvik","family":"Anil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,5]]},"reference":[{"key":"2912_CR1","first-page":"2015","volume":"1\u201312","author":"T Akhavan","year":"2015","unstructured":"Akhavan, T., Kaufmann, H.: Backward compatible HDR stereo matching: a hybrid tone-mapping-based framework. EURASIP JIVP 1\u201312, 2015 (2015)","journal-title":"EURASIP JIVP"},{"key":"2912_CR2","unstructured":"Akhavan, T., Yoo, H., Gelautz, M.: A framework for HDR stereo matching using multi-exposed images. In: HDRi2013, pp. 1\u20134 (2013)"},{"key":"2912_CR3","unstructured":"Alhashim, I., Wonka, P.: High quality monocular depth estimation via transfer learning. arXiv: 1812.11941 (2018)"},{"key":"2912_CR4","doi-asserted-by":"crossref","unstructured":"Anil, R., Sharma, M., Choudhary, R.: Sde-dualenet: a novel dual efficient convolutional neural network for robust stereo depth estimation. In: 2021 International Conference on Visual Communications and Image Processing (VCIP), pp. 1\u20135 (2021)","DOI":"10.1109\/VCIP53242.2021.9675391"},{"issue":"4","key":"2912_CR5","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","volume":"31","author":"P Burt","year":"1983","unstructured":"Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532\u2013540 (1983)","journal-title":"IEEE Trans. Commun."},{"key":"2912_CR6","doi-asserted-by":"crossref","unstructured":"Cantrell, K.J., Miller, C.D., Morato, C.W.: Practical depth estimation with image segmentation and serial U-Nets. In: VEHITS, vol. I, pp. 406\u2013414. INSTICC, SciTePress (2020)","DOI":"10.5220\/0009781804060414"},{"key":"2912_CR7","doi-asserted-by":"crossref","unstructured":"Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: IEEE CVPR 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"2912_CR8","unstructured":"Chari, P., Vadathya, A.K., Mitra, K.: Optimal HDR and depth from dual cameras. arXiv:2003.05907 (2020)"},{"key":"2912_CR9","doi-asserted-by":"crossref","unstructured":"Choudhary, R., Sharma, M., Uma, T.V., Anil, R.: Mestereo-du2cnn: a novel dual channel CNN for learning robust depth estimates from multi-exposure stereo images for HDR 3d applications. arXiv preprint arXiv:2206.10375 (2022)","DOI":"10.1007\/s00371-023-02912-z"},{"key":"2912_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2912_CR11","unstructured":"Diebel, J., Thrun, S.: An application of Markov random fields to range sensing. Adv. Neural Inf. Process. Syst. 18 (2005)"},{"key":"2912_CR12","doi-asserted-by":"crossref","unstructured":"Duggal, S., Wang, S., Ma, W.-C., Hu, R., Urtasun, R.: Deeppruner: learning efficient stereo matching via differentiable patchmatch. In ICCV (2019)","DOI":"10.1109\/ICCV.2019.00448"},{"key":"2912_CR13","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advance Neural Information Processing System, vol.\u00a027, Curran Associates, Inc. (2014)"},{"issue":"6","key":"2912_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130800.3130816","volume":"36","author":"G Eilertsen","year":"2017","unstructured":"Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R., Unger, J.: HDR image reconstruction from a single exposure using deep CNNs. ACM Trans. Graph. 36(6), 1\u20135 (2017)","journal-title":"ACM Trans. Graph."},{"key":"2912_CR15","doi-asserted-by":"crossref","unstructured":"Farooq Bhat, S., Alhashim, I., Wonka, P.: Adabins: depth estimation using adaptive bins. In: 2021 IEEE\/CVF CVPR, pp. 4008\u20134017 (2021)","DOI":"10.1109\/CVPR46437.2021.00400"},{"key":"2912_CR16","doi-asserted-by":"crossref","unstructured":"Ferstl, D., Ruther, M., Bischof, H.: Variational depth superresolution using example-based edge representations. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 513\u2013521 (2015)","DOI":"10.1109\/ICCV.2015.66"},{"issue":"5","key":"2912_CR17","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/JSTSP.2012.2207090","volume":"6","author":"F Garcia","year":"2012","unstructured":"Garcia, F., Aouada, D., Mirbach, B., Ottersten, B.: Real-time distance-dependent mapping for a hybrid ToF multi-camera rig. IEEE J. Sel. Top. Signal Process. 6(5), 425\u2013436 (2012)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"2912_CR18","doi-asserted-by":"crossref","unstructured":"Hao, Z., Li, Y., You, S., Lu, F.: Detail preserving depth estimation from a single image using attention guided networks. In: 2018 International Conference on 3D Vision (3DV), pp. 304\u2013313 (2018)","DOI":"10.1109\/3DV.2018.00043"},{"key":"2912_CR19","doi-asserted-by":"crossref","unstructured":"Hasinoff, S.W., Durand, F., Freeman, W.T.: Noise-optimal capture for high dynamic range photography. In: IEEE CVPR, pp. 553\u2013560 (2010)","DOI":"10.1109\/CVPR.2010.5540167"},{"issue":"2","key":"2912_CR20","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","volume":"30","author":"H Hirschmuller","year":"2008","unstructured":"Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328\u2013341 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR21","doi-asserted-by":"crossref","unstructured":"Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: IEEE CVPR, pp. 1\u20138 (2007). https:\/\/vision.middlebury.edu\/stereo\/data\/scenes2006\/","DOI":"10.1109\/CVPR.2007.383248"},{"key":"2912_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Ozay, M., Zhang, Y., Okatani, T.: Revisiting single image depth estimation: toward higher resolution maps with accurate object boundaries. In: IEEE WACV (2019)","DOI":"10.1109\/WACV.2019.00116"},{"key":"2912_CR23","doi-asserted-by":"crossref","unstructured":"Hui, T.-W., Loy, C.C., Tang, X.: Depth map super-resolution by deep multi-scale guidance. In: European conference on computer vision, pp. 353\u2013369. Springer (2016)","DOI":"10.1007\/978-3-319-46487-9_22"},{"issue":"5","key":"2912_CR24","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/TIP.2018.2886777","volume":"28","author":"S Im","year":"2019","unstructured":"Im, S., Jeon, H.-G., Kweon, I.S.: Robust depth estimation using auto-exposure bracketing. IEEE Trans. Image Process. 28(5), 2451\u20132464 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"2912_CR25","unstructured":"Supplementary information\u00a0file. Available:\u00a0tinyurl.com\/2p95b2dz"},{"issue":"19","key":"2912_CR26","doi-asserted-by":"publisher","first-page":"7388","DOI":"10.3390\/s22197388","volume":"22","author":"Y Jeong","year":"2022","unstructured":"Jeong, Y., Park, J., Cho, D., Hwang, Y., Choi, S.B., Kweon, I.S.: Lightweight depth completion network with local similarity-preserving knowledge distillation. Sensors 22(19), 7388 (2022)","journal-title":"Sensors"},{"issue":"4","key":"2912_CR27","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1145\/3072959.3073609","volume":"36","author":"NK Kalantari","year":"2017","unstructured":"Kalantari, N.K., Ramamoorthi, R.: Deep high dynamic range imaging of dynamic scenes. ACM Trans. Graph. 36(4), 144 (2017)","journal-title":"ACM Trans. Graph."},{"key":"2912_CR28","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry P., Kennedy, R.: Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 66\u201375 (2017)","DOI":"10.1109\/ICCV.2017.17"},{"key":"2912_CR29","unstructured":"Kingma, D. P., Ba, J.: Adam: a method for stochastic optimization. Preprint arXiv:1412.6980 (2014)"},{"key":"2912_CR30","doi-asserted-by":"crossref","unstructured":"Kopf, J., Cohen, M., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Gr. (ToG) 26(3), 96\u2013es (2007)","DOI":"10.1145\/1276377.1276497"},{"key":"2912_CR31","doi-asserted-by":"crossref","unstructured":"Kwon, H., Tai, Y.-W., Lin, S.: Data-driven depth map refinement via multi-scale sparse representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 159\u2013167 (2015)","DOI":"10.1109\/CVPR.2015.7298611"},{"key":"2912_CR32","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 3DV, pp. 239\u2013248 (2016)","DOI":"10.1109\/3DV.2016.32"},{"issue":"9\u201311","key":"2912_CR33","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1007\/s00371-021-02206-2","volume":"37","author":"Y Li","year":"2021","unstructured":"Li, Y., Luo, F., Li, W., Zheng, S., Huan-huan, W., Xiao, C.: Self-supervised monocular depth estimation based on image texture detail enhancement. Vis. Comput. 37(9\u201311), 2567\u20132580 (2021)","journal-title":"Vis. Comput."},{"key":"2912_CR34","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F.X., Taylor, R.H., Unberath, M.: Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. arXiv:2011.02910 (2020)","DOI":"10.1109\/ICCV48922.2021.00614"},{"key":"2912_CR35","doi-asserted-by":"crossref","unstructured":"Liang, Z., Feng, Y., Guo, Y., Liu, H., Chen, W., Qiao, L., Zhou, L., Zhang, J.: Learning for disparity estimation through feature constancy. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2811\u20132820 (2018)","DOI":"10.1109\/CVPR.2018.00297"},{"key":"2912_CR36","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: IEEE CVPR 5168\u20135177 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"2912_CR37","doi-asserted-by":"crossref","unstructured":"Lin, H.-Y., Kao, C.-C.: Stereo matching techniques for high dynamic range image pairs. In: Image and Video Technology 605\u2013616 (2016)","DOI":"10.1007\/978-3-319-29451-3_48"},{"key":"2912_CR38","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE CVPR, pp. 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2912_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Y.-L., Lai, W.-S., Chen, Y.-S., Kao, Y.-L., Yang, M.-H., Chuang, Y.-Y., Huang, J.-B.: Single-image HDR reconstruction by learning to reverse the camera pipeline. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1648\u20131657 (2020)","DOI":"10.1109\/CVPR42600.2020.00172"},{"issue":"5","key":"2912_CR40","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1364\/JOSAA.7.000923","volume":"7","author":"J Malik","year":"1990","unstructured":"Malik, J., Perona, P.: Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A 7(5), 923\u2013932 (1990)","journal-title":"J. Opt. Soc. Am. A"},{"key":"2912_CR41","doi-asserted-by":"crossref","unstructured":"Mayer, N., Ilg, E., H\u00e4usser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE CVPR, pp. 4040\u20134048 (2016). https:\/\/lmb.informatik.uni-freiburg.de\/resources\/datasets\/SceneFlowDatasets.en.html","DOI":"10.1109\/CVPR.2016.438"},{"key":"2912_CR42","doi-asserted-by":"crossref","unstructured":"Mertens, T., Kautz, J., Van Reeth, F.: Exposure fusion. In: 15th Pacific Conference on Computer Graphics and Applications (PG\u201907), pp. 382\u2013390 (2007)","DOI":"10.1109\/PG.2007.17"},{"issue":"3","key":"2912_CR43","first-page":"1176","volume":"21","author":"D Min","year":"2011","unstructured":"Min, D., Jiangbo, L., Do, M.N.: Depth video enhancement based on weighted mode filtering. IEEE Trans. Image Process. 21(3), 1176\u20131190 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"2912_CR44","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TIP.2015.2395820","volume":"24","author":"MG Mozerov","year":"2015","unstructured":"Mozerov, M.G., van de Weijer, J.: Accurate stereo matching by two-step energy minimization. IEEE Trans. Image Process. 24(3), 1153\u20131163 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"2912_CR45","first-page":"198","volume":"9","author":"A Nayana","year":"2015","unstructured":"Nayana, A., Johnson, A.K.: High dynamic range imaging-a review. Int. J. Image Process. 9(4), 198 (2015)","journal-title":"Int. J. Image Process."},{"key":"2912_CR46","doi-asserted-by":"crossref","unstructured":"Ning, S., Xu, H., Song, L., Xie, R., Zhang, W.: Learning an inverse tone mapping network with a generative adversarial regularizer. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1383\u20131387 (2018)","DOI":"10.1109\/ICASSP.2018.8462444"},{"issue":"2","key":"2912_CR47","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TPAMI.1985.4767639","volume":"7","author":"Y Ohta","year":"1985","unstructured":"Ohta, Y., Kanade, T.: Stereo by intra- and inter-scanline search using dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 7(2), 139\u2013154 (1985)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. PAMI"},{"key":"2912_CR48","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE TPAMI 1 (2020)"},{"key":"2912_CR49","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ferstl, D., R\u00fcther, M., Bischof, H.: A deep primal-dual network for guided depth super-resolution. Preprint arXiv: 1607.08569 (2016)","DOI":"10.5244\/C.30.7"},{"key":"2912_CR50","doi-asserted-by":"crossref","unstructured":"Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, Steve, F., Dustin, D., Andrew, et al.: Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559\u2013568 (2011)","DOI":"10.1145\/2047196.2047270"},{"key":"2912_CR51","doi-asserted-by":"crossref","unstructured":"Scharstein, D., Szeliski, R., Zabih, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In: Proceedings IEEE Workshop on Stereo and Multi-baseline Vision (SMBV 2001), pp. 131\u2013140 (2001)","DOI":"10.1109\/SMBV.2001.988771"},{"key":"2912_CR52","doi-asserted-by":"crossref","unstructured":"Scharstein, D., Hirschm\u00fcller, H., Kitajima, Y., Krathwohl, G., Ne\u0161i\u0107, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Xiaoyi Jiang, Joachim Hornegger, and Reinhard Koch, editors, Pattern Recognition, pp. 31\u201342 (2014). https:\/\/vision.middlebury.edu\/stereo\/data\/scenes2014","DOI":"10.1007\/978-3-319-11752-2_3"},{"key":"2912_CR53","doi-asserted-by":"crossref","unstructured":"Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: IEEE CVPR, pp. 1\u20138 (2007). https:\/\/vision.middlebury.edu\/stereo\/data\/scenes2005\/","DOI":"10.1109\/CVPR.2007.383191"},{"key":"2912_CR54","doi-asserted-by":"crossref","unstructured":"Schuon, S., Theobalt, C., Davis, J., Thrun, S.: Lidarboost: depth superresolution for tof 3d shape scanning. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 343\u2013350. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206804"},{"key":"2912_CR55","doi-asserted-by":"crossref","unstructured":"Sharma, M., Sharma, A., Tushar, K.R., Panneer, A.: A novel 3d-unet deep learning framework based on high-dimensional bilateral grid for edge consistent single image depth estimation. In: IC3D, pp. 01\u201308 (2020)","DOI":"10.1109\/IC3D51119.2020.9376327"},{"key":"2912_CR56","unstructured":"Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv: 1905.11946 (2019)"},{"key":"2912_CR57","doi-asserted-by":"crossref","unstructured":"Wadaskar, A., Sharma, M., Lal, R.: A rich stereoscopic 3d high dynamic range image & video database of natural scenes. In IC3D, pp. 1\u20138 (2019). https:\/\/ieeexplore.ieee.org\/document\/8975903","DOI":"10.1109\/IC3D48390.2019.8975903"},{"key":"2912_CR58","unstructured":"Wang, L., Yoon, K.-J..: Deep learning for HDR imaging: state-of-the-art and future trends. IEEE Transactions on Pattern Analysis and Machine Intelligence 1 (2021)"},{"key":"2912_CR59","doi-asserted-by":"crossref","unstructured":"Watson, J., Firman, M., Brostow, G.J., Turmukhambetov, D.: Self-supervised monocular depth hints. In: IEEE ICCV (2019)","DOI":"10.1109\/ICCV.2019.00225"},{"key":"2912_CR60","doi-asserted-by":"crossref","unstructured":"Xian, K., Shen, C., Cao, Z., Hao, L., Xiao, Y., Li, R., Luo, Z.: Monocular relative depth perception with web stereo data supervision. In: IEEE CVPR 311\u2013320 (2018)","DOI":"10.1109\/CVPR.2018.00040"},{"key":"2912_CR61","doi-asserted-by":"crossref","unstructured":"Xu, D., Ricci, E., Ouyang, W., Wang, X., Sebe, N.: Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 161\u2013169 (2017)","DOI":"10.1109\/CVPR.2017.25"},{"key":"2912_CR62","doi-asserted-by":"crossref","unstructured":"Xu, D., Wang, W., Tang, H., Liu, H., Sebe, N., Ricci, E.: Structured attention guided convolutional neural fields for monocular depth estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3917\u20133925 (2018)","DOI":"10.1109\/CVPR.2018.00412"},{"key":"2912_CR63","doi-asserted-by":"crossref","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)","DOI":"10.1109\/3DV53792.2021.00056"},{"key":"2912_CR64","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1109\/TIP.2020.2971346","volume":"29","author":"Q Yan","year":"2020","unstructured":"Yan, Q., Zhang, L., Liu, Yu., Zhu, Yu., Sun, J., Shi, Q., Zhang, Y.: Deep HDR imaging via a non-local network. IEEE Trans. Image Process. 29, 4308\u20134322 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2912_CR65","doi-asserted-by":"crossref","unstructured":"Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: IEEE CVPR 5510\u20135519 (2019)","DOI":"10.1109\/CVPR.2019.00566"},{"issue":"11","key":"2912_CR66","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861\u20132873 (2010)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"2912_CR67","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1109\/TIP.2014.2329776","volume":"23","author":"J Yang","year":"2014","unstructured":"Yang, J., Ye, X., Li, K., Hou, C., Wang, Y.: Color-guided depth recovery from RGB-D data using an adaptive autoregressive model. IEEE Trans. Image Process. 23(8), 3443\u20133458 (2014)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"2912_CR68","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s00371-021-02092-8","volume":"38","author":"T Zhao","year":"2022","unstructured":"Zhao, T., Pan, S., Gao, W., Sheng, C., Sun, Y., Wei, J.: Attention unet++ for lightweight depth estimation from sparse depth samples and a single RGB image. Vis. Comput. 38(5), 1619\u20131630 (2022)","journal-title":"Vis. Comput."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02912-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02912-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02912-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T17:16:20Z","timestamp":1729703780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02912-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,5]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2912"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02912-z","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,5]]},"assertion":[{"value":"27 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2023","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}