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Neural networks are highly effective tools for image reconstruction problems such as denoising and compressive sensing.
“A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics”
D. Martin, C. Fowlkes, D. Tal and J. Malik · 2001
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“Image Quality Assessment: From Error Visibility to Structural Similarity”
Zhou Wang, A.. Bovik, H.. Sheikh and E.. Simoncelli · 2004
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“Imagenet: A Large-Scale Hierarchical Image Database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Image Denoising: Can Plain Neural Networks Compete with BM3D?”
H.. Burger, C.. Schuler and S. Harmeling · 2012
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“Image demosaicking by nonlocal adaptive thresholding”
Sandip. Kasar and Sachin. Ruikar · 2013
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“Single Image Super-Resolution From Transformed Self-Exemplars”
Jia-Bin Huang, Abhishek Singh and Narendra Ahuja · 2015
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“U-Net: Convolutional Networks for Biomedical Image Segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
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“Deep Convolutional Neural Network for Inverse Problems in Imaging”
K.. Jin, M.. McCann, E. Froustey and M. Unser · 2017
Earlier work this paper cites.
“Convolutional Neural Networks for Inverse Problems in Imaging: A Review”
Michael. McCann, Kyong Jin and Michael Unser · 2017
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“Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”
K. Zhang, W. Zuo, Y. Chen, D. Meng and L. Zhang · 2017
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“A High-Quality Denoising Dataset for Smartphone Cameras”
Abdelrahman Abdelhamed, Stephen Lin and Michael. Brown · 2018
Cited alongside, same era.
“Deep Image Prior”
Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky · 2018
Cited alongside, same era.
“fastMRI: An Open Dataset and Benchmarks for Accelerated MRI”
Jure Zbontar et al · 2018
Cited alongside, same era.
“Unprocessing Images for Learned Raw Denoising”
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet and Jonathan. Barron · 2019
Cited alongside, same era.
“Deep Decoder: Concise Image Representations from Untrained Non-Convolutional Networks”
Reinhard Heckel and Paul Hand · 2019
Cited alongside, same era.
“Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators”
“MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation”
George Cazenavette and Manuel De · 2021
Later among the works it cites.
“CycleMLP: A MLP-like Architecture for Dense Prediction”
Shoufa Chen, Enze Xie, Chongjian Ge, Ding Liang and Ping Luo · 2021
Later among the works it cites.
“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”
Alexey Dosovitskiy et al · 2021
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Hanxiao Liu, Zihang Dai, David. So and Quoc. Le · 2021
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“Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows”
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo · 2021
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Reinhard Heckel and Mahdi Soltanolkotabi · 2020
Cited alongside, same era.
“Advancing Machine Learning for MR Image Reconstruction with an Open Competition: Overview of the 2019 fastMRI Challenge”
Florian Knoll et al · 2020
Cited alongside, same era.
“End-to-End Variational Networks for Accelerated MRI Reconstruction”
Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Zitnick, Nafissa Yakubova, Florian Knoll and Patricia Johnson · 2020
Cited alongside, same era.
“Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation”
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian and Manning Wang · 2021
Cited alongside, same era.
Later among the works it cites.
“RaftMLP: Do MLP-Based Models Dream of Winning Over Computer Vision?”
Yuki Tatsunami and Masato Taki · 2021
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“MLP-Mixer: An All-MLP Architecture for Vision”
Ilya Tolstikhin et al · 2021
Later among the works it cites.
“ResMLP: Feedforward Networks for Image Classification with Data-Efficient Training”
Hugo Touvron et al · 2021
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“Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering”
K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian · 2095
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