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Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
k-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
M. Aharon, M. Elad, and A. Bruckstein · 2006
Earlier work this paper cites.
Image upsampling via imposed edge statistics
R. Fattal · 2007
Earlier work this paper cites.
Image super-resolution via sparse representation
J. Yang, J. Wright, T. S. Huang, and Y. Ma · 2010
Earlier work this paper cites.
Modeling the performance of image restoration from motion blur
G. Boracchi and A. Foi · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
Earlier work this paper cites.
Unnatural L0 sparse representation for natural image deblurring
L. Xu, S. Zheng, and J. Jia · 2013
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Total variation blind deconvolution: The devil is in the details
D. Perrone and P. Favaro · 2014
Earlier work this paper cites.
Compression artifacts reduction by a deep convolutional network
C. Dong, Y. Deng, C. Change Loy, and X. Tang · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
Earlier work this paper cites.
Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Earlier work this paper cites.
Learning a convolutional neural network for non-uniform motion blur removal
J. Sun, W. Cao, Z. Xu, and J. Ponce · 2015
Earlier work this paper cites.
Building dual-domain representations for compression artifacts reduction
J. Guo and H. Chao · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
X. Mao, C. Shen, and Y. Yang · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
Cited alongside, same era.
Globally and locally consistent image completion
S. Iizuka, E. Simo-Serra, and H. Ishikawa · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
Non-locally enhanced encoder-decoder network for single image de-raining
G. Li, X. He, W. Zhang, H. Chang, L. Dong, and L. Lin · 2018
Closest in time.
Tell me where to look: Guided attention inference network
K. Li, Z. Wu, K.-C. Peng, J. Ernst, and Y. Fu · 2018
Closest in time.
Recurrent squeeze-and-excitation context aggregation net for single image deraining
X. Li, J. Wu, Z. Lin, H. Liu, and H. Zha · 2018
Closest in time.
Image inpainting for irregular holes using partial convolutions
G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro · 2018
Closest in time.
Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
Closest in time.
Picanet: Learning pixel-wise contextual attention for saliency detection
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Cited alongside, same era.
Deep multi-scale convolutional neural network for dynamic scene deblurring
S. Nah, T. H. Kim, and K. M. Lee · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Residual attention network for image classification
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, and X. Tang · 2017
Cited alongside, same era.
Deep joint rain detection and removal from a single image
W. Yang, R. T. Tan, J. Feng, J. Liu, Z. Guo, and S. Yan · 2017
Cited alongside, same era.
N. Liu, J. Han, and M.-H. Yang · 2018
Closest in time.
Improved fusion of visual and language representations by dense symmetric co-attention for visual question answering
D.-K. Nguyen and T. Okatani · 2018
Closest in time.
Bam: bottleneck attention module
J. Park, S. Woo, J.-Y. Lee, and I. S. Kweon · 2018
Closest in time.
Attentive generative adversarial network for raindrop removal from a single image
R. Qian, R. T. Tan, W. Yang, J. Su, and J. Liu · 2018
Closest in time.
Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search
M. Suganuma, M. Ozay, and T. Okatani · 2018
Closest in time.
Convolutional networks with adaptive inference graphs
A. Veit and S. Belongie · 2018
Closest in time.
Cbam: Convolutional block attention module
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon · 2018
Closest in time.
Crafting a toolchain for image restoration by deep reinforcement learning
K. Yu, C. Dong, L. Lin, and L. C. Change · 2018
Closest in time.
Density-aware single image de-raining using a multi-stream dense network
H. Zhang and V. M. Patel · 2018
Closest in time.
Ffdnet: Toward a fast and flexible solution for cnn based image denoising
K. Zhang, W. Zuo, and L. Zhang · 2018
Closest in time.
Learning a single convolutional super-resolution network for multiple degradations
K. Zhang, W. Zuo, and L. Zhang · 2018
Closest in time.
Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
Closest in time.
Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
Closest in time.