Fetching the paper…
Reading the bibliography…
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model.
Ray tracing volume densities
James T Kajiya and Brian P Von Herzen · 1984
Earlier work this paper cites.
Compositing digital images
Thomas Porter and Tom Duff · 1984
Earlier work this paper cites.
Optical models for direct volume rendering
Nelson Max · 1995
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Tricubic interpolation in three dimensions
Francois Lekien and J Marsden · 2005
Earlier work this paper cites.
Health effects of ionising radiation from diagnostic ct
Diego R Martin and Richard C Semelka · 2006
Earlier work this paper cites.
Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain mri
Ali Gholipour, Judy A Estroff, and Simon K Warfield · 2010
Earlier work this paper cites.
Combining short-axis and long-axis cardiac mr images by applying a super-resolution reconstruction algorithm
Stefan Wesarg et al · 2010
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun · 2011
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi-level densely connected network
Yuhua Chen, Feng Shi, Anthony G Christodoulou, Yibin Xie, Zhengwei Zhou, and Debiao Li · 2018
Earlier work this paper cites.
“zero-shot” super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
Earlier work this paper cites.
Deep mr brain image super-resolution using spatio-structural priors
Venkateswararao Cherukuri, Tiantong Guo, Steven J Schiff, and Vishal Monga · 2019
Earlier work this paper cites.
Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, et al · 2019
Cited alongside, same era.
Meta-sr: A magnification-arbitrary network for super-resolution
Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun · 2019
Cited alongside, same era.
Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al · 2019
Cited alongside, same era.
Ct super-resolution gan constrained by the identical, residual, and cycle learning ensemble (gan-circle)
Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Mengzhou Li, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, et al · 2019
Cited alongside, same era.
Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
Later among the works it cites.
pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
Later among the works it cites.
Intratomo: self-supervised learning-based tomography via sinogram synthesis and prediction
Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, and Wolfgang Heidrich · 2021
Later among the works it cites.
Neural rgb-d surface reconstruction
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies · 2022
Later among the works it cites.
Mip-nerf 360: Unbounded anti-aliased neural radiance fields
Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman · 2022
Later among the works it cites.
Videoinr: Learning video implicit neural representation for continuous space-time super-resolution
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Channel splitting network for single mr image super-resolution
Xiaole Zhao, Yulun Zhang, Tao Zhang, and Xueming Zou · 2019
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
Cited alongside, same era.
Saint: Spatially aware interpolation network for medical slice synthesis
Cheng Peng, Wei-An Lin, Haofu Liao, Rama Chellappa, and S. Kevin Zhou · 2020
Cited alongside, same era.
Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
Cited alongside, same era.
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
Cited alongside, same era.
Enhanced generative adversarial network for 3d brain mri super-resolution
Jiancong Wang, Yuhua Chen, Yifan Wu, Jianbo Shi, and James Gee · 2020
Cited alongside, same era.
Deep learning for image super-resolution: A survey
Zhihao Wang, Jian Chen, and Steven CH Hoi · 2020
Cited alongside, same era.
Nerf-pytorch
Lin Yen-Chen · 2020
Cited alongside, same era.
Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang · 2022
Later among the works it cites.
Mednerf: Medical neural radiance fields for reconstructing 3d-aware ct-projections from a single x-ray, 2022
Abril Corona-Figueroa, Jonathan Frawley, Sam Bond-Taylor, Sarath Bethapudi, Hubert P. H. Shum, and Chris G. Willcocks · 2022
Later among the works it cites.
Depth-supervised nerf: Fewer views and faster training for free
Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan · 2022
Later among the works it cites.
Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
Later among the works it cites.
Uassr: Unsupervised arbitrary scale super-resolution reconstruction of single anisotropic 3d images via disentangled representation learning
Jiale Wang, Runze Wang, Rong Tao, and Guoyan Zheng · 2022
Later among the works it cites.
Rplhr-ct dataset and transformer baseline for volumetric super-resolution from ct scans
Pengxin Yu, Haoyue Zhang, Han Kang, Wen Tang, Corey W Arnold, and Rongguo Zhang · 2022
Later among the works it cites.
Aprf: Anti-aliasing projection representation field for inverse problem in imaging
Zixuan Chen, Lingxiao Yang, Jianhuang Lai, and Xiaohua Xie · 2023
Closest in time.
Implicit neural representation in medical imaging: A comparative survey
Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli, Amirhossein Kazerouni, Bobby Azad, Reza Azad, and Dorit Merhof · 2023
Closest in time.
Sparsenerf: Distilling depth ranking for few-shot novel view synthesis
Guangcong Wang, Zhaoxi Chen, Chen Change Loy, and Ziwei Liu · 2023
Closest in time.
An arbitrary scale super-resolution approach for 3d mr images via implicit neural representation
Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, and Yuyao Zhang · 2023
Closest in time.