Fetching the paper…
Reading the bibliography…
An approach to incorporate deep learning within an iterative image reconstruction framework to reconstruct images from severely incomplete measurement data is presented.
Fundamentals of Digital Image Processing
Anil K. Jain · 1989
Earlier work this paper cites.
Foundations of image science
H.H. Barrett and K.J. Myers · 2004
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J. Candès, Justin K. Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization
Emil Y Sidky and Xiaochuan Pan · 2008
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Deep unfolding: Model-based inspiration of novel deep architectures
John R. Hershey, Jonathan Le Roux, and Felix Weninger · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning Optimal Nonlinearities for Iterative Thresholding Algorithms
U. S. Kamilov and H. Mansour · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Reconnet: Non-iterative reconstruction of images from compressively sensed random measurements
Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, and Amit Ashok · 2016
Cited alongside, same era.
Deep admm-net for compressive sensing mri
Yan Yang, Jian Sun, Huibin Li, and Zongben Xu · 2016
Later among the works it cites.
Semantic image inpainting with perceptual and contextual losses
Raymond Yeh, Chen Chen, Teck Yian Lim, Mark Hasegawa-Johnson, and Minh N Do · 2016
Later among the works it cites.
Deep learning for photoacoustic tomography from sparse data
Stephan Antholzer, Markus Haltmeier, and Johannes Schwab · 2017
Closest in time.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
Closest in time.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang · 2016
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
Cited alongside, same era.
A perspective on deep imaging
Ge Wang · 2016
Cited alongside, same era.
Accelerating magnetic resonance imaging via deep learning
Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, Dagan Feng, and Dong Liang · 2016
Cited alongside, same era.
Low-dose ct with a residual encoder-decoder convolutional neural network (red-cnn)
Hu Chen, Yi Zhang, Mannudeep K Kalra, Feng Lin, Peixi Liao, Jiliu Zhou, and Ge Wang · 2017
Closest in time.
A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction
Kerstin Hammernik, Tobias Würfl, Thomas Pock, and Andreas Maier · 2017
Closest in time.
Globally and Locally Consistent Image Completion
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa · 2017
Closest in time.
Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
Tim Meinhardt, Michael Möller, Caner Hazirbas, and Daniel Cremers · 2017
Closest in time.
Learning the weight matrix for sparsity averaging in compressive imaging
Dimitris Perdios, Adrien Georges Jean Besson, Philippe Rossinelli, and Jean-Philippe Thiran · 2017
Closest in time.