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Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Colorization using optimization
Anat Levin, Dani Lischinski, and Yair Weiss · 2004
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Digital photography with flash and no-flash image pairs
Georg Petschnigg, Richard Szeliski, Maneesh Agrawala, Michael Cohen, Hugues Hoppe, and Kentaro Toyama · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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An application of markov random fields to range sensing
James Diebel and Sebastian Thrun · 2006
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Joint bilateral upsampling
Johannes Kopf, Michael F Cohen, Dani Lischinski, and Matt Uyttendaele · 2007
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Feature-preserving non-local denoising of static and time-varying range data
Oliver Schall, Alexander Belyaev, and Hans-Peter Seidel · 2007
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Learning conditional random fields for stereo
Daniel Scharstein and Chris Pal · 2007
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Spatial-depth super resolution for range images
Qingxiong Yang, Ruigang Yang, James Davis, and David Nistér · 2007
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Robust non-local denoising of colored depth data
Benjamin Huhle, Timo Schairer, Philipp Jenke, and Wolfgang Straßer · 2008
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Guided image filtering
Kaiming He, Jian Sun, and Xiaoou Tang · 2010
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Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma · 2010
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High quality depth map upsampling for 3d-tof cameras
Jaesik Park, Hyeongwoo Kim, Yu-Wing Tai, Michael S. Brown, and In-So Kweon · 2011
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Depth image super-resolution using multi-dictionary sparse representation
Haoheng Zheng, Abdesselam Bouzerdoum, and Son Lam Phung · 2011
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A naturalistic open source movie for optical flow evaluation
Daniel J Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black · 2012
Cited alongside, same era.
Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
Cited alongside, same era.
Image guided depth upsampling using anisotropic total generalized variation
David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Rüther, and Horst Bischof · 2013
Cited alongside, same era.
Depth super resolution by rigid body self-similarity in 3d
Michael Hornacek, Christoph Rhemann, Margrit Gelautz, and Carsten Rother · 2013
Cited alongside, same era.
A joint intensity and depth co-sparse analysis model for depth map super-resolution
Martin Kiechle, Simon Hawe, and Martin Kleinsteuber · 2013
Cited alongside, same era.
Joint geodesic upsampling of depth images
Ming-Yu Liu, Oncel Tuzel, and Yuichi Taguchi · 2013
Fast guided global interpolation for depth and motion
Yu Li, Dongbo Min, Minh N Do, and Jiangbo Lu · 2016
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A deep primal-dual network for guided depth super-resolution
Gernot Riegler, David Ferstl, Matthias Rüther, and Horst Bischof · 2016
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Edge-guided single depth image super resolution
Jun Xie, Rogério Schmidt Feris, and Ming-Ting Sun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Cited alongside, same era.
A consensus-driven approach for structure and texture aware depth map upsampling
Ouk Choi and Seung-Won Jung · 2014
Cited alongside, same era.
High-resolution stereo datasets with subpixel-accurate ground truth
Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nešić, Xi Wang, and Porter Westling · 2014
Cited alongside, same era.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Cited alongside, same era.
Data-driven depth map refinement via multi-scale sparse representation
HyeokHyen Kwon, Yu-Wing Tai, and Stephen Lin · 2015
Cited alongside, same era.
Sparse depth super resolution
Jiajun Lu and David Forsyth · 2015
Cited alongside, same era.
Loss functions for neural networks for image processing
Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz · 2015
Cited alongside, same era.
S. Mandal, A. Bhavsar, and A. K. Sao · 2017
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Depth super-resolution meets uncalibrated photometric stereo
Songyou Peng, Bjoern Haefner, Yvain Quéau, and Daniel Cremers · 2017
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Hierarchical features driven residual learning for depth map super-resolution
Chunle Guo, Chongyi Li, Jichang Guo, Runmin Cong, Huazhu Fu, and Ping Han · 2018
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Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
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Feedback network for image super-resolution
Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, and Wei Wu · 2019
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Perceptual deep depth super-resolution
Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Evgeny Burnaev, and Denis Zorin · 2019
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Channel attention based iterative residual learning for depth map super-resolution
Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li, Hongdong Li, and Ruigang Yang · 2020
Closest in time.
Depth upsampling based on deep edge-aware learning
Zhihui Wang, Xinchen Ye, Baoli Sun, Jingyu Yang, Rui Xu, and Haojie Li · 2020
Closest in time.