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}

@article{gharbi2017deep,
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  pages={118},
  year={2017},
  publisher={ACM}
}

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  pages={1-8}
}

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  pages={1-8}
}

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}

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  year={2016},
  pages={4086-4094}
}

@InProceedings{Agustsson_2017_CVPR_Workshops,
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	title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
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	month = {July},
	year = {2017}
} 

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}

@article{VillenaMartinez2020WhenDL,
  title={When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)},
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  journal={ArXiv},
  year={2020},
  volume={abs/2003.03167}
}



@article{Long2016LearningIM,
  title={Learning Image Matching by Simply Watching Video},
  author={Gucan Long and Laurent Kneip and Jose M. Alvarez and Hongdong Li and Xiaohu Zhang and Qifeng Yu},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.06041}
}

@inproceedings{Choi2020ChannelAI,
  title={Channel Attention Is All You Need for Video Frame Interpolation},
  author={Minh Yuk Choi and Hee-won Kim and Bohyung Han and Ning Xu and Kyoung Mu Lee},
  booktitle={AAAI},
  year={2020}
}

@article{Shi2016RealTimeSI,
  title={Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network},
  author={Wenzhe Shi and Jose Caballero and Ferenc Husz{\'a}r and Johannes Totz and Andrew P. Aitken and Rob Bishop and Daniel Rueckert and Zehan Wang},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1874-1883}
}
@inproceedings{zhang2018rcan,
    title={Image Super-Resolution Using Very Deep Residual Channel Attention Networks},
    author={Zhang, Yulun and Li, Kunpeng and Li, Kai and Wang, Lichen and Zhong, Bineng and Fu, Yun},
    booktitle={ECCV},
    year={2018}
}

@InProceedings{xiang2020zooming,
  author = {Xiang, Xiaoyu and Tian, Yapeng and Zhang, Yulun and Fu, Yun and Allebach, Jan P. and Xu, Chenliang},
  title = {Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={3370--3379},
  month = {June},
  year = {2020}
}


@inproceedings{Niklaus_ICCV_2017,
         author = {Simon Niklaus and Long Mai and Feng Liu},
         title = {Video Frame Interpolation via Adaptive Separable Convolution},
         booktitle = {IEEE International Conference on Computer Vision},
         year = {2017}
     }
s
@inproceedings{liu2017voxelflow,
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 title = {Video Frame Synthesis using Deep Voxel Flow},
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 month = {October},
 year = {2017} 
}

@article{Jiang2018SuperSH,
  title={Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation},
  author={Huaizu Jiang and Deqing Sun and Varun Jampani and Ming-Hsuan Yang and Erik G. Learned-Miller and Jan Kautz},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={9000-9008}
}

@article{Bao2018MEMCNet,
     title={MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement},
     author={Bao, Wenbo and Lai, Wei-Sheng and Zhang, Xiaoyun and Gao, Zhiyong and Yang, Ming-Hsuan},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
     doi={10.1109/TPAMI.2019.2941941},
     year={2018}
}

@inproceedings{STAR2020,
  title={Space-Time-Aware Multi-Resolution Video Enhancement},
  author={Haris, Muhammad and Shakhnarovich, Greg and Ukita, Norimichi},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

@article{Dosovitskiy2015FlowNetLO,
  title={FlowNet: Learning Optical Flow with Convolutional Networks},
  author={Alexey Dosovitskiy and Philipp Fischer and Eddy Ilg and Philip H{\"a}usser and Caner Hazirbas and Vladimir Golkov and Patrick van der Smagt and Daniel Cremers and Thomas Brox},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={2758-2766}
}
@article{Ilg2017FlowNet2E,
  title={FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks},
  author={Eddy Ilg and Nikolaus Mayer and Tonmoy Saikia and Margret Keuper and Alexey Dosovitskiy and Thomas Brox},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={1647-1655}
}

@article{Sun2018PWCNetCF,
  title={PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume},
  author={Deqing Sun and Xiaodong Yang and Ming-Yu Liu and Jan Kautz},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={8934-8943}
}

@article{Hur2019IterativeRR,
  title={Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation},
  author={Junhwa Hur and Stefan Roth},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={5747-5756}
}

@article{Jeon2018EnhancingTS,
  title={Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior},
  author={Daniel S. Jeon and Seung-Hwan Baek and Inchang Choi and Min H. Kim},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={1721-1730}
}


@article{Wang2019LearningPA,
  title={Learning Parallax Attention for Stereo Image Super-Resolution},
  author={Longguang Wang and Yingqian Wang and Zhengfa Liang and Zaiping Lin and Jungang Yang and Wei An and Yulan Guo},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={12242-12251}
}


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  author={Sung Cheol Park and Min Kyu Park and Moon Gi Kang},
  journal={IEEE Signal Processing Magazine},
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  volume={20},
  pages={21-36}
}

@inproceedings{Zheng2018CrossNetAE,
  title={CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping},
  author={Haitian Zheng and Mengqi Ji and Haoqian Wang and Yebin Liu and Lu Fang},
  booktitle={ECCV},
  year={2018}
}

@article{Yue2013LandmarkIS,
  title={Landmark Image Super-Resolution by Retrieving Web Images},
  author={Huanjing Yue and Xiaoyan Sun and Jing-yu Yang and Feng Wu},
  journal={IEEE Transactions on Image Processing},
  year={2013},
  volume={22},
  pages={4865-4878}
}


@article{Zhang2019ImageSB,
  title={Image Super-Resolution by Neural Texture Transfer},
  author={Zhifei Zhang and Zhaowen Wang and Zhe L. Lin and Hairong Qi},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={7974-7983}
}


@InProceedings{yang2020learning,
author = {Yang, Fuzhi and Yang, Huan and Fu, Jianlong and Lu, Hongtao and Guo, Baining},
title = {Learning Texture Transformer Network for Image Super-Resolution},
booktitle = {CVPR},
year = {2020},
month = {June}
}
@article{Kim2016AccurateIS,
  title={Accurate Image Super-Resolution Using Very Deep Convolutional Networks},
  author={Jiwon Kim and Jung Kwon Lee and Kyoung Mu Lee},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1646-1654}
}

@article{Kim2016DeeplyRecursiveCN,
  title={Deeply-Recursive Convolutional Network for Image Super-Resolution},
  author={Jiwon Kim and Jung Kwon Lee and Kyoung Mu Lee},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1637-1645}
}

@article{Zhang2018ResidualDN,
  title={Residual Dense Network for Image Super-Resolution},
  author={Yulun Zhang and Yapeng Tian and Yu Kong and Bineng Zhong and Yun Fu},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={2472-2481}
}
@inproceedings{Liu2018NonLocalRN,
  title={Non-Local Recurrent Network for Image Restoration},
  author={Ding Liu and Bihan Wen and Yuchen Fan and Chen Change Loy and Thomas S. Huang},
  booktitle={NeurIPS},
  year={2018}
}

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  title={Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification},
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  journal={ACM Trans. Graph.},
  year={2016},
  volume={35},
  pages={110:1-110:11}
}

@inproceedings{zhang2016colorful,
  title={Colorful Image Colorization},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
  booktitle={ECCV},
  year={2016}
}

@article{Zhao2018PixellevelSG,
  title={Pixel-level Semantics Guided Image Colorization},
  author={Jiaojiao Zhao and Li Liu and Cees G. M. Snoek and Jungong Han and Ling Shao},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.01597}
}

@inproceedings{Su-CVPR-2020,
  author = {Su, Jheng-Wei and Chu, Hung-Kuo and Huang, Jia-Bin},
  title = {Instance-aware Image Colorization},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}
}


@article{Zhang2017RealtimeUI,
  title={Real-time user-guided image colorization with learned deep priors},
  author={Richard Zhang and Jun-Yan Zhu and Phillip Isola and Xinyang Geng and Angela S. Lin and Tianhe Yu and Alexei A. Efros},
  journal={ACM Trans. Graph.},
  year={2017},
  volume={36},
  pages={119:1-119:11}
}

@article{Xiao2019InteractiveDC,
  title={Interactive Deep Colorization Using Simultaneous Global and Local Inputs},
  author={Yongguang Xiao and Peiyao Zhou and Yan Zheng},
  journal={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2019},
  pages={1887-1891}
}

@inproceedings{Dong2019LearningAD,
  title={Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System},
  author={Xuan Dong and Weixin Li and Xiaojie Wang and Yunhong Wang},
  booktitle={AAAI},
  year={2019}
}

@article{He2018DeepEC,
  title={Deep exemplar-based colorization},
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  journal={ACM Transactions on Graphics (TOG)},
  year={2018},
  volume={37},
  pages={1 - 16}
}

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  title={Deep Exemplar-Based Video Colorization},
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  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={8044-8053}
}

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}


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}

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  year={2011}
}


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  title={A low-light image enhancement method for both denoising and contrast enlarging},
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  pages={3730-3734}
}


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  year={2017},
  volume={26},
  pages={982-993}
}


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  pages={7-23}
}

@article{Fu2013AVF,
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}

@inproceedings{Chen2018Retinex,

  title={Deep Retinex Decomposition for Low-Light Enhancement},

  author={Chen Wei, Wenjing Wang, Wenhan Yang and Jiaying Liu},

  booktitle={British Machine Vision Conference},

  year={2018},

} 

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  title={Self-supervised Image Enhancement Network: Training with Low Light Images Only},
  author={Yifan Zhang and Xiao-Guang Di and Bin Zhang and Chunhui Wang},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.11300}
}

@article{Wang2019RDGANRD,
  title={RDGAN: Retinex Decomposition Based Adversarial Learning for Low-Light Enhancement},
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  year={2019},
  pages={1186-1191}
}

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  title={Kindling the Darkness: A Practical Low-light Image Enhancer},
  author={Yonghua Zhang and Jiawan Zhang and Xiaojie Guo},
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  year={2019}
}


@article{lore2017llnet,
  title={LLNet: A deep autoencoder approach to natural low-light image enhancement},
  author={Lore, Kin Gwn and Akintayo, Adedotun and Sarkar, Soumik},
  journal={Pattern Recognition},
  volume={61},
  pages={650--662},
  year={2017},
  publisher={Elsevier}
}


@article{George2019On,
author = {George Barbastathis and Aydogan Ozcan and Guohai Situ},
journal = {Optica},
keywords = {Computational imaging; Image processing; Image quality; Imaging systems; Phase imaging; Phase retrieval},
number = {8},
pages = {921--943},
publisher = {OSA},
title = {On the use of deep learning for computational imaging},
volume = {6},
month = {Aug},
year = {2019},
url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-6-8-921},
doi = {10.1364/OPTICA.6.000921},
abstract = {Since their inception in the 1930\&\#x2013;1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or uncertain. Examples reviewed here are: super-resolution; lensless retrieval of phase and complex amplitude from intensity; photon-limited scenes, including ghost imaging; and imaging through scatter. In this paper, we cast these works in a common framework. We relate the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability. We also explore how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe. It is hoped that the present unifying exposition will stimulate further progress in this promising field of research.},
}

@article{Wang2019StereoscopicDF,
  title={Stereoscopic Dark Flash for Low-light Photography},
  author={J. J. Wang and Tianfan Xue and Jonathan T. Barron and Jiawen Chen},
  journal={2019 IEEE International Conference on Computational Photography (ICCP)},
  year={2019},
  pages={1-10}
}

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  title={Multi-View Image Fusion},
  author={Marc Comino Trinidad and Ricardo Martin-Brualla and Florian Kainz and Janne Kontkanen},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={4100-4109}
}

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  year={2019},
  publisher={IGI Global}
}

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and Krathwohl, Greg
and Ne{\v{s}}i{\'{c}}, Nera
and Wang, Xi
and Westling, Porter",
editor="Jiang, Xiaoyi
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and Koch, Reinhard",
title="High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth",
booktitle="Pattern Recognition",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="31--42",
abstract="We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.",
isbn="978-3-319-11752-2"
}

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  year={2019},
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  pages={912-923}
}

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 booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)},
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 month = {June},
 year = {2020}
}


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  author    = "N. Mayer and E. Ilg and P. H{\"a}usser and P. Fischer and D. Cremers and A. Dosovitskiy and T. Brox",
  title     = "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation",
  booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
  year      = "2016",
  note      = "arXiv:1512.02134",
  url       = "http://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16"
}

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  pages={3538-3548}
} 

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  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={2782-2790}
} 


@webpage{insta360,
title={Insta360:https://www.insta360.com/product/insta360-titan/},
url = {https://www.insta360.com/product/insta360-titan/}
}

@webpage{mantis,
title={mantis:https://www.aqueti.com/technology/},
url = {https://www.aqueti.com/technology/}
}


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@article{chu2019fast,
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  year={2019}
}

@article{wronski2019handheld,
  title={Handheld multi-frame super-resolution},
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  number={4},
  pages={1--18},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{godard2018deep,
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  pages={538--554},
  year={2018}
}

@inproceedings{ignatov2020replacing,
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  year={2020}
}

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  year={2019},
  publisher={Elsevier}
}


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