-
4
-
-
34247277702
-
-
SanRafael, CA: Morgan & Claypool
-
A. K. Katsaggelos, R. Molina, J. Mateos, Super Resolution of Images andVideo: Synthesis Lectures on Image, Video, Multimedia Processing. SanRafael, CA: Morgan & Claypool, 2007.
-
(2007)
Super Resolution of Images AndVideo: Synthesis Lectures On Image, Video, Multimedia Processing
-
-
Katsaggelos, A.K.1
Molina, R.2
Mateos, J.3
-
5
-
-
84901054636
-
Variational Bayesianmethods for multimedia problems
-
Z. Chen, S. D. Babacan, R. Molina, A. K. Katsaggelos, "Variational Bayesianmethods for multimedia problems," IEEE Trans. Multimedia, vol. 16, no. 4, pp. 1000-10017, 2014.
-
(2014)
IEEE Trans. Multimedia
, vol.16
, Issue.4
, pp. 1000-10017
-
-
Chen, Z.1
Babacan, S.D.2
Molina, R.3
Katsaggelos, A.K.4
-
7
-
-
33646818674
-
Image denoising using a neural network based non-linearfilter in wavelet domain
-
Mar.
-
S. Zhang and E. Salari, "Image denoising using a neural network based non-linearfilter in wavelet domain," in Proc. IEEE Int. Conf. Acoustics, Speech, SignalProcessing, Mar. 2005, pp. 989-992.
-
(2005)
Proc. IEEE Int. Conf. Acoustics, Speech, SignalProcessing
, pp. 989-992
-
-
Zhang, S.1
Salari, E.2
-
9
-
-
84887391162
-
A machine learningapproach for non-blind image deconvolution
-
C. J. Schuler, H. C. Burger, S. Harmeling, B. Scholkopf, "A machine learningapproach for non-blind image deconvolution," in Proc. IEEE Conf. Computer VisionPattern Recognition, 2013, pp. 1067-1074.
-
(2013)
Proc. IEEE Conf. Computer VisionPattern Recognition
, pp. 1067-1074
-
-
Schuler, C.J.1
Burger, H.C.2
Harmeling, S.3
Scholkopf, B.4
-
10
-
-
84877728447
-
Image denoising and inpainting with deep neural networks
-
Dec.
-
J. Xie, L. Xu, E. Chen, "Image denoising and inpainting with deep neural networks,"Adv. Neural Inform. Process. Syst., vol. 25, pp. 341-349 Dec. 2012.
-
(2012)
Adv. Neural Inform. Process. Syst.
, vol.25
, pp. 341-349
-
-
Xie, J.1
Xu, L.2
Chen, E.3
-
11
-
-
84899024949
-
Adaptive multicolumn deep neuralnetworks with application to robust image denoising
-
F. Agostinelli, M. R. Anderson, H. Lee, "Adaptive multicolumn deep neuralnetworks with application to robust image denoising," in Proc. Neural InformationProcessing Systems Conf., 2013, pp. 1-9.
-
(2013)
Proc. Neural InformationProcessing Systems Conf.
, pp. 1-9
-
-
Agostinelli, F.1
Anderson, M.R.2
Lee, H.3
-
12
-
-
0025627940
-
Universal approximation of anunknown mapping and its derivatives using multilayer feedforward networks
-
K. Hornik, M. Stinchcombe, H. White, "Universal approximation of anunknown mapping and its derivatives using multilayer feedforward networks," NeuralNetw., vol. 3, no. 5, pp. 551-560, 1990.
-
(1990)
NeuralNetw.
, vol.3
, Issue.5
, pp. 551-560
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
15
-
-
84898798806
-
Restoring an image taken through a windowcovered with dirt or rain
-
D. Eigen, D. Krishnan, R. Fergus, "Restoring an image taken through a windowcovered with dirt or rain," in Proc. IEEE Int. Conf. Computer Vision, 2013, pp.633-640.
-
(2013)
Proc. IEEE Int. Conf. Computer Vision
, pp. 633-640
-
-
Eigen, D.1
Krishnan, D.2
Fergus, R.3
-
16
-
-
84962128851
-
Image super-resolution using deep convolutionalnetworks
-
Feb.
-
C. Dong, C. C. Loy, K. He, X. Tang, "Image super-resolution using deep convolutionalnetworks," IEEE Trans. Pattern Anal. Machine Intell., vol. 38, no. 2, pp.295-307, Feb. 2016.
-
(2016)
IEEE Trans. Pattern Anal. Machine Intell.
, vol.38
, Issue.2
, pp. 295-307
-
-
Dong, C.1
Loy, C.C.2
He, K.3
Tang, X.4
-
17
-
-
85140805648
-
Video super-resolution withconvolutional neural networks
-
A. Kappeler, S. Yoo, Q. Dai, A. K. Katsaggelos, "Video super-resolution withconvolutional neural networks," IEEE Trans. Computational Imaging, vol. 2, no. 2, pp.109-122, 2016.
-
(2016)
IEEE Trans. Computational Imaging
, vol.2
, Issue.2
, pp. 109-122
-
-
Kappeler, A.1
Yoo, S.2
Dai, Q.3
Katsaggelos, A.K.4
-
18
-
-
84986249771
-
Reconnet: Noniterativereconstruction of images from compressively sensed measurements
-
June
-
K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, A. Ashok, "Reconnet: Noniterativereconstruction of images from compressively sensed measurements," in Proc.IEEE Conf. Computer Vision Pattern Recognition, June 2016, pp. 449-458.
-
(2016)
Proc. IEEE Conf. Computer Vision Pattern Recognition
, pp. 449-458
-
-
Kulkarni, K.1
Lohit, S.2
Turaga, P.3
Kerviche, R.4
Ashok, A.5
-
19
-
-
84990058751
-
Convolutional neural networksfor direct text deblurring
-
M. Hradiš, J. Kotera, P. Zemcík, F. Šroubek, "Convolutional neural networksfor direct text deblurring," in Proc. British Machine Vision Conf., 2015, vol. 10, pp.1-13.
-
(2015)
Proc. British Machine Vision Conf.
, vol.10
, pp. 1-13
-
-
Hradiš, M.1
Kotera, J.2
Zemcík, P.3
Šroubek, F.4
-
20
-
-
77956509090
-
Rectified linear units improve restricted Boltzmannmachines
-
V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmannmachines," in Proc. 27th Int. Conf. Machine Learning, 2010, pp. 807-814.
-
(2010)
Proc. 27th Int. Conf. Machine Learning
, pp. 807-814
-
-
Nair, V.1
Hinton, G.E.2
-
21
-
-
84973911419
-
Delving deep into rectifiers: Surpassinghuman-level performance on imagenet classification
-
K. He, X. Zhang, S. Ren, J. Sun, "Delving deep into rectifiers: Surpassinghuman-level performance on imagenet classification," in Proc. IEEE Int. Conf.Computer Vision, Washington, DC, 2015, pp. 1026-1034.
-
(2015)
Proc. IEEE Int. Conf.Computer Vision, Washington, DC
, pp. 1026-1034
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
22
-
-
84969584486
-
Batch normalization: Accelerating deep network trainingby reducing internal covariate shift
-
S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network trainingby reducing internal covariate shift," in Proc. Int. Conf. Machine Learning, 2015, pp.448-456.
-
(2015)
Proc. Int. Conf. Machine Learning
, pp. 448-456
-
-
Ioffe, S.1
Szegedy, C.2
-
23
-
-
84986274465
-
Deep residual learning for image recognition
-
June
-
K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition,"in Proc. IEEE Conf. Computer Vision Pattern Recognition, June 2016, pp. 770-778.
-
(2016)
Proc. IEEE Conf. Computer Vision Pattern Recognition
, pp. 770-778
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
25
-
-
85019017178
-
-
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Aitken, A. Tejani, J. Totz, Z.Wang, W. Shi, "Photo-realistic single image super-resolution using a generativeadversarial network," arXiv preprint, arXiv:1609.04802, 2016.
-
(2016)
Photo-realistic Single Image Super-resolution Using a Generativeadversarial Network
-
-
Ledig, C.1
Theis, L.2
Huszar, F.3
Caballero, J.4
Aitken, A.5
Tejani, A.6
Totz, J.7
Wang, Z.8
Shi, W.9
-
26
-
-
84986325587
-
Accurate image super-resolution using verydeep convolutional networks
-
J. Kim, J. K. Lee, K. M. Lee, "Accurate image super-resolution using verydeep convolutional networks," in Proc. IEEE Conf. Computer Vision PatternRecognition, 2016, pp. 1646-1654.
-
(2016)
Proc. IEEE Conf. Computer Vision PatternRecognition
, pp. 1646-1654
-
-
Kim, J.1
Lee, J.K.2
Lee, K.M.3
-
27
-
-
85037997015
-
-
H. Yao, F. Dai, D. Zhang, Y. Ma, S. Zhang, Y. Zhang, "Dr2-net: Deep residualreconstruction network for image compressive sensing," arXiv preprint,arXiv:1702.05743, 2017.
-
(2017)
Dr2-net: Deep Residualreconstruction Network for Image Compressive Sensing
-
-
Yao, H.1
Dai, F.2
Zhang, D.3
Ma, Y.4
Zhang, S.5
Zhang, Y.6
-
28
-
-
85021724055
-
Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising
-
Feb.
-
K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, "Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising," IEEE Trans. ImageProcessing, vol. 26, no. 2, pp. 1-13, Feb. 2017.
-
(2017)
IEEE Trans. ImageProcessing
, vol.26
, Issue.2
, pp. 1-13
-
-
Zhang, K.1
Zuo, W.2
Chen, Y.3
Meng, D.4
Zhang, L.5
-
29
-
-
84951834022
-
U-Net: Convolutional networks for biomedicalimage segmentation
-
O. Ronneberger, P. Fischer, T. Brox, "U-Net: Convolutional networks for biomedicalimage segmentation," in Proc. Int. Conf. Medical Image Computing andComputer-Assisted Intervention, 2015, pp. 234-241.
-
(2015)
Proc. Int. Conf. Medical Image Computing AndComputer-Assisted Intervention
, pp. 234-241
-
-
Ronneberger, O.1
Fischer, P.2
Brox, T.3
-
31
-
-
84986294165
-
Context encoders:Feature learning by inpainting
-
June
-
D. Pathak, P. Krhenbhl, J. Donahue, T. Darrell, A. A. Efros, "Context encoders:Feature learning by inpainting," in Proc. IEEE Conf. Computer Vision PatternRecognition, June 2016, pp. 2536-2544.
-
(2016)
Proc. IEEE Conf. Computer Vision PatternRecognition
, pp. 2536-2544
-
-
Pathak, D.1
Krhenbhl, P.2
Donahue, J.3
Darrell, T.4
Efros, A.A.5
-
32
-
-
84973904859
-
Flownet: Learning optical flow with convolutionalnetworks
-
P. Fischer, A. Dosovitskiy, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. v. d.Smagt, D. Cremers, T. Brox, "Flownet: Learning optical flow with convolutionalnetworks," in Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 2758-2766.
-
(2015)
Proc. IEEE Int. Conf. Computer Vision
, pp. 2758-2766
-
-
Fischer, P.1
Dosovitskiy, A.2
Ilg, E.3
Hausser, P.4
Hazirbas, C.5
Golkov, V.6
Smagt, P.V.D.7
Cremers, D.8
Brox, T.9
-
33
-
-
85023170573
-
Deep convolutional neural networkfor inverse problems in imaging
-
K. Jin, M. McCann, E. Froustey, M. Unser, "Deep convolutional neural networkfor inverse problems in imaging," IEEE Trans. Image Processing, vol. 26, no. 9,pp. 4509-4522, 2017.
-
(2017)
IEEE Trans. Image Processing
, vol.26
, Issue.9
, pp. 4509-4522
-
-
Jin, K.1
McCann, M.2
Froustey, E.3
Unser, M.4
-
34
-
-
79551480483
-
Stackeddenoising autoencoders: Learning useful representations in a deep network with a localdenoising criterion
-
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, "Stackeddenoising autoencoders: Learning useful representations in a deep network with a localdenoising criterion," J. Machine Learning Res., vol. 11, pp. 3371-3408, 2010.
-
(2010)
J. Machine Learning Res.
, vol.11
, pp. 3371-3408
-
-
Vincent, P.1
Larochelle, H.2
Lajoie, I.3
Bengio, Y.4
Manzagol, P.-A.5
-
35
-
-
84950145705
-
Deep network cascade forimage super-resolution
-
Z. Cui, H. Chang, S. Shan, B. Zhong, X. Chen, "Deep network cascade forimage super-resolution," in Proc. European Conf. Computer Vision, 2016, pp. 49-64.
-
(2016)
Proc. European Conf. Computer Vision
, pp. 49-64
-
-
Cui, Z.1
Chang, H.2
Shan, S.3
Zhong, B.4
Chen, X.5
-
37
-
-
84949845174
-
Coupled deep autoencoder for singleimage super-resolution
-
K. Zeng, J. Yu, R. Wang, C. Li, D. Tao, "Coupled deep autoencoder for singleimage super-resolution," IEEE Trans. Cybern., vol. 47, no. 1, pp. 27-37, 2017.
-
(2017)
IEEE Trans. Cybern.
, vol.47
, Issue.1
, pp. 27-37
-
-
Zeng, K.1
Yu, J.2
Wang, R.3
Li, C.4
Tao, D.5
-
39
-
-
84969776710
-
A deep learning approach to structuredsignal recovery
-
vol. abs/1508.04065
-
A. Mousavi, A. B. Patel, R. G. Baraniuk, "A deep learning approach to structuredsignal recovery," in Proc. Communication, Control Computing (Allerton) 53rdAnnu. Allerton Conf., 2015, vol. abs/1508.04065, pp. 1336-1343.
-
(2015)
Proc. Communication, Control Computing (Allerton) 53rdAnnu. Allerton Conf.
, pp. 1336-1343
-
-
Mousavi, A.1
Patel, A.B.2
Baraniuk, R.G.3
-
40
-
-
85037727145
-
-
A. Bora, A. Jalal, E. Price, A. G. Dimakis, "Compressed sensing using generativemodels," arXiv preprint, arXiv:1703.03208, 2017.
-
(2017)
Compressed Sensing Using Generativemodels
-
-
Bora, A.1
Jalal, A.2
Price, E.3
Dimakis, A.G.4
-
41
-
-
85022205772
-
-
Cambridge, U.K.: Cambridge Univ.Press
-
R. Borhani, J. Watt, A. K. Katsaggelos, Machine Learning Refined:Foundations, Algorithms, Applications. Cambridge, U.K.: Cambridge Univ.Press, 2016.
-
(2016)
Machine Learning Refined: Foundations, Algorithms, Applications
-
-
Borhani, R.1
Watt, J.2
Katsaggelos, A.K.3
-
42
-
-
84990854047
-
Perceptual losses for real-time style transferand super-resolution
-
J. Johnson, A. Alahi, L. Fei-Fei, "Perceptual losses for real-time style transferand super-resolution," in Proc. European Conf. Computer Vision, 2016, pp. 694-711.
-
(2016)
Proc. European Conf. Computer Vision
, pp. 694-711
-
-
Johnson, J.1
Alahi, A.2
Fei-Fei, L.3
-
43
-
-
85083954075
-
Super-resolution with deep convolutionalsufficient statistics
-
J. Bruna, P. Sprechmann, Y. LeCun, "Super-resolution with deep convolutionalsufficient statistics," in Proc. Int. Conf. Learning Representations, 2016, pp. 1-17.
-
(2016)
Proc. Int. Conf. Learning Representations
, pp. 1-17
-
-
Bruna, J.1
Sprechmann, P.2
LeCun, Y.3
-
44
-
-
85019248552
-
Trainable nonlinear reaction diffusion: A flexible frameworkfor fast and effective image restoration
-
Y. Chen and T. Pock, "Trainable nonlinear reaction diffusion: A flexible frameworkfor fast and effective image restoration," IEEE Trans. Pattern Anal. MachineIntell., vol. 39, no. 6, pp. 1256-1272, 2015.
-
(2015)
IEEE Trans. Pattern Anal. MachineIntell.
, vol.39
, Issue.6
, pp. 1256-1272
-
-
Chen, Y.1
Pock, T.2
-
45
-
-
84937878882
-
Deep convolutional neural network for imagedeconvolution
-
Dec.
-
L. Xu, J. S. Ren, C. Liu, J. Jia, "Deep convolutional neural network for imagedeconvolution," Adv. Neural Inform. Process. Syst., vol. 27, pp. 1790-1798, Dec. 2014.
-
(2014)
Adv. Neural Inform. Process. Syst.
, vol.27
, pp. 1790-1798
-
-
Xu, L.1
Ren, J.S.2
Liu, C.3
Jia, J.4
-
46
-
-
84973897612
-
Deep networks for imagesuper-resolution with sparse prior
-
Z. Wang, D. Liu, J. Yang, W. Han, T. Huang, "Deep networks for imagesuper-resolution with sparse prior," in Proc. IEEE Int. Conf. Computer Vision, 2015,pp. 370-378.
-
(2015)
Proc. IEEE Int. Conf. Computer Vision
, pp. 370-378
-
-
Wang, Z.1
Liu, D.2
Yang, J.3
Han, W.4
Huang, T.5
-
49
-
-
85018917190
-
Deep ADMM-net for compressive sensingMRI
-
Y. Yang, J. Sun, H. Li, Z. Xu, "Deep ADMM-net for compressive sensingMRI," in Proc. Neural Information Processing Systems Conf., 2016, pp. 10-18.
-
(2016)
Proc. Neural Information Processing Systems Conf.
, pp. 10-18
-
-
Yang, Y.1
Sun, J.2
Li, H.3
Xu, Z.4
-
50
-
-
77955783919
-
Fast image recoveryusing variable splitting and constrained optimization
-
M. V. Afonso, J. M. Bioucas-Dias, M. A. T. Figueiredo, "Fast image recoveryusing variable splitting and constrained optimization," IEEE Trans. Image Processing,vol. 19, no. 3, pp. 2345-2356, 2010.
-
(2010)
IEEE Trans. Image Processing
, vol.19
, Issue.3
, pp. 2345-2356
-
-
Afonso, M.V.1
Bioucas-Dias, J.M.2
Figueiredo, M.A.T.3
-
51
-
-
84976521325
-
Learning to deblur
-
C. J. Schuler, M. Hirsch, S. Harmeling, B. Schlkopf, "Learning to deblur,"IEEE Trans. Pattern Anal. Machine Intell., vol. 38, no. 7, pp. 1439-1451, 2016.
-
(2016)
IEEE Trans. Pattern Anal. Machine Intell.
, vol.38
, Issue.7
, pp. 1439-1451
-
-
Schuler, C.J.1
Hirsch, M.2
Harmeling, S.3
Schlkopf, B.4
-
52
-
-
85039953013
-
-
K. Zhang, W. Zuo, S. Gu, L. Zhang, "Learning deep CNN denoiser prior forimage restoration," arXiv preprint, arXiv:1704.03264, 2017.
-
(2017)
Learning Deep CNN Denoiser Prior Forimage Restoration
-
-
Zhang, K.1
Zuo, W.2
Gu, S.3
Zhang, L.4
-
53
-
-
85030249566
-
-
J. H. R. Chang, C.-L. Li, B. Poczos, V. K. Vijaya Kumar, A. C.Sankaranarayanan, "One network to solve them all: Solving linear inverse problemsusing deep projection models," arXiv preprint, arXiv:1703.09912, 2017.
-
(2017)
One Network to Solve Them All: Solving Linear Inverse Problemsusing Deep Projection Models
-
-
Chang, J.H.R.1
Li, C.-L.2
Poczos, B.3
Vijaya Kumar, V.K.4
Sankaranarayanan, A.C.5
-
54
-
-
84937849144
-
Generative adversarial nets
-
Dec.
-
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.Courville, Y. Bengio, "Generative adversarial nets," Adv. Neural Inform. Process.Syst., vol. 26, pp. 2672-2680, Dec. 2014.
-
(2014)
Adv. Neural Inform. Process.Syst.
, vol.26
, pp. 2672-2680
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
55
-
-
85081923574
-
Amortised MAPinference for image super-resolution
-
C. K. Sonderby, J. Caballero, L. Theis, W. Shi, F. Huszár, "Amortised MAPinference for image super-resolution," in Proc. Int. Conf. Learning Representations,2017, pp. 1-17.
-
(2017)
Proc. Int. Conf. Learning Representations
, pp. 1-17
-
-
Sonderby, C.K.1
Caballero, J.2
Theis, L.3
Shi, W.4
Huszár, F.5
|