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Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task.
Intriguing findings of frequency selection for image deblurring
Mao, X.; Liu, Y.; Liu, F.; Li, Q.; Shen, W.; and Wang, Y. 2023 · 1913
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Joint image filtering with deep convolutional networks
Li, Y.; Huang, J.-B.; Ahuja, N.; and Yang, M.-H. 2019 · 1923
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Learning graph regularisation for guided super-resolution
De Lutio, R.; Becker, A.; D’Aronco, S.; Russo, S.; Wegner, J. D.; and Schindler, K. 2022 · 1988
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Evaluation of cost functions for stereo matching
Hirschmuller, H.; and Scharstein, D. 2007 · 2007
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Learning conditional random fields for stereo
Scharstein, D.; and Pal, C. 2007 · 2007
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Gradient profile prior and its applications in image super-resolution and enhancement
Sun, J.; Xu, Z.; and Shum, H.-Y. 2010 · 2010
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Indoor segmentation and support inference from rgbd images
Silberman, N.; Hoiem, D.; Kohli, P.; and Fergus, R. 2012 · 2012
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Image guided depth upsampling using anisotropic total generalized variation
Ferstl, D.; Reinbacher, C.; Ranftl, R.; Rüther, M.; and Bischof, H. 2013 · 2013
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Depth enhancement via low-rank matrix completion
Lu, S.; Ren, X.; and Liu, F. 2014 · 2014
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Modeling deformable gradient compositions for single-image super-resolution
Zhu, Y.; Zhang, Y.; Bonev, B.; and Yuille, A. L. 2015 · 2015
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Depth map super-resolution by deep multi-scale guidance
Hui, T.-W.; Loy, C. C.; and Tang, X. 2016 · 2016
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Deep joint image filtering
Li, Y.; Huang, J.-B.; Ahuja, N.; and Yang, M.-H. 2016 · 2016
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Robust guided image filtering using nonconvex potentials
Ham, B.; Cho, M.; and Ponce, J. 2017 · 2017
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Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda
Bonetti, F.; Warnaby, G.; and Quinn, L. 2018 · 2018
Cited alongside, same era.
Cbam: Convolutional block attention module
Woo, S.; Park, J.; Lee, J.-Y.; and Kweon, I. S. 2018 · 2018
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Image super-resolution using very deep residual channel attention networks
Zhang, Y.; Li, K.; Li, K.; Wang, L.; Zhong, B.; and Fu, Y. 2018 · 2018
Cited alongside, same era.
Guided image-to-image translation with bi-directional feature transformation
AlBahar, B.; and Huang, J.-B. 2019 · 2019
Cited alongside, same era.
Pixel-adaptive convolutional neural networks
Su, H.; Jampani, V.; Sun, D.; Gallo, O.; Learned-Miller, E.; and Kautz, J. 2019 · 2019
Learning scene structure guidance via cross-task knowledge transfer for single depth super-resolution
Sun, B.; Ye, X.; Li, B.; Li, H.; Wang, Z.; and Xu, R. 2021 · 2021
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Joint implicit image function for guided depth super-resolution
Tang, J.; Chen, X.; and Zeng, G. 2021 · 2021
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Bridgenet: A joint learning network of depth map super-resolution and monocular depth estimation
Tang, Q.; Cong, R.; Sheng, R.; He, L.; Zhang, D.; Zhao, Y.; and Kwong, S. 2021 · 2021
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Augmented reality and virtual reality displays: emerging technologies and future perspectives
Xiong, J.; Hsiang, E.-L.; He, Z.; Zhan, T.; and Wu, S.-T. 2021 · 2021
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High-resolution depth maps imaging via attention-based hierarchical multi-modal fusion
Zhong, Z.; Liu, X.; Jiang, J.; Zhao, D.; Chen, Z.; and Ji, X. 2021 · 2021
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Cited alongside, same era.
Deep convolutional neural network for multi-modal image restoration and fusion
Deng, X.; and Dragotti, P. L. 2020 · 2020
Cited alongside, same era.
Structure-preserving super resolution with gradient guidance
Ma, C.; Rao, Y.; Cheng, Y.; Chen, C.; Lu, J.; and Zhou, J. 2020 · 2020
Cited alongside, same era.
Channel attention based iterative residual learning for depth map super-resolution
Song, X.; Dai, Y.; Zhou, D.; Liu, L.; Li, W.; Li, H.; and Yang, R. 2020 · 2020
Cited alongside, same era.
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
He, L.; Zhu, H.; Li, F.; Bai, H.; Cong, R.; Zhang, C.; Lin, C.; Liu, M.; and Zhao, Y. 2021 · 2021
Cited alongside, same era.
Focal frequency loss for image reconstruction and synthesis
Jiang, L.; Dai, B.; Wu, W.; and Loy, C. C. 2021 · 2021
Cited alongside, same era.
Deformable kernel networks for joint image filtering
Kim, B.; Ponce, J.; and Ham, B. 2021 · 2021
Cited alongside, same era.
Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution
Shi, W.; Ye, M.; and Du, B. 2022 · 2022
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CODON: on orchestrating cross-domain attentions for depth super-resolution
Yang, Y.; Cao, Q.; Zhang, J.; and Tao, D. 2022 · 2022
Later among the works it cites.
Discrete cosine transform network for guided depth map super-resolution
Zhao, Z.; Zhang, J.; Xu, S.; Lin, Z.; and Pfister, H. 2022 · 2022
Later among the works it cites.
Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder
Lin, X.; Li, Y.; Hsiao, J.; Ho, C.; and Kong, Y. 2023 · 2023
Closest in time.
Guided Depth Super-Resolution by Deep Anisotropic Diffusion
Metzger, N.; Daudt, R. C.; and Schindler, K. 2023 · 2023
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Depth Super-Resolution from Explicit and Implicit High-Frequency Features
Qiao, X.; Ge, C.; Zhang, Y.; Zhou, Y.; Tosi, F.; Poggi, M.; and Mattoccia, S. 2023 · 2023
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Spherical space feature decomposition for guided depth map super-resolution
Zhao, Z.; Zhang, J.; Gu, X.; Tan, C.; Xu, S.; Zhang, Y.; Timofte, R.; and Van Gool, L. 2023 · 2023
Closest in time.