Fetching the paper…
Reading the bibliography…
Deep learning techniques have received much attention in the area of image denoising.
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
1906
Earlier work this paper cites.
1912
Earlier work this paper cites.
1912
Earlier work this paper cites.
Gondara, L., Wang, K., 2017. Recovering loss to followup information using denoising autoencoders. In: 2017 IEEE International Conference on Big Data (Big Data). IEEE, pp. 1936–1945
1945
Earlier work this paper cites.
Ioffe, S., 2017. Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. In: Advances In Neural Information Processing Systems. pp. 1945–1953
1953
Earlier work this paper cites.
HUANG, T., 1971. Stability of two-dimensional recursive filters(mathematical model for stability problem in two-dimensional recursive filtering)
1971
Earlier work this paper cites.
Fukushima, K., 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36 (4), 193–202
1980
Earlier work this paper cites.
Fukushima, K., Miyake, S., 1982. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and Cooperation in Neural Nets. Springer, pp. 267–285
1982
Earlier work this paper cites.
Li, H., Yang, W., Yong, X., 2018a. Deep learning for ground-roll noise attenuation. In: SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, pp. 1981–1985
1985
Earlier work this paper cites.
Pitas, I., Venetsanopoulos, A., 1986. Nonlinear mean filters in image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing 34 (3), 573–584
1986
Earlier work this paper cites.
Bernstein, R., 1987. Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images. IEEE Transactions on Circuits and Systems 34 (11), 1275–1291
1987
Earlier work this paper cites.
Zhou, Y., Chellappa, R., Jenkins, B., 1987. A novel approach to image restoration based on a neural network. In: Proceedings of the International Conference on Neural Networks, San Diego, California
1987
Earlier work this paper cites.
Chiang, Y.-W., Sullivan, B., 1989. Multi-frame image restoration using a neural network. In: Proceedings of the 32nd Midwest Symposium on Circuits and Systems,. IEEE, pp. 744–747
1989
Earlier work this paper cites.
Bedini, L., Tonazzini, A., 1990. Neural network use in maximum entropy image restoration. Image and Vision Computing 8 (2), 108–114
1990
Earlier work this paper cites.
Si, X., Yuan, Y., 2018. Random noise attenuation based on residual learning of deep convolutional neural network. In: SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, pp. 1986–1990
1990
Earlier work this paper cites.
Hirose, Y., Yamashita, K., Hijiya, S., 1991. Back-propagation algorithm which varies the number of hidden units. Neural Networks 4 (1), 61–66
1991
Earlier work this paper cites.
Bedini, L., Tonazzini, A., 1992. Image restoration preserving discontinuities: the bayesian approach and neural networks. Image and Vision Computing 10 (2), 108–118
1992
Earlier work this paper cites.
de Figueiredo, M. T., Leitao, J. M., 1992. Image restoration using neural networks. In: [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 2. IEEE, pp. 409–412
1992
Earlier work this paper cites.
Paik, J. K., Katsaggelos, A. K., 1992. Image restoration using a modified hopfield network. IEEE Transactions on Image Processing 1 (1), 49–63
1992
Earlier work this paper cites.
Nossek, J., Roska, T., 1993. Special issue on cellular neural networks-introduction
1993
Earlier work this paper cites.
Greenhill, D., Davies, E., 1994. Relative effectiveness of neural networks for image noise suppression. In: Machine Intelligence and Pattern Recognition. Vol. 16. Elsevier, pp. 367–378
1994
Earlier work this paper cites.
Lo, S.-C., Lou, S.-L., Lin, J.-S., Freedman, M. T., Chien, M. V., Mun, S. K., 1995. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging 14 (4), 711–718
1995
Earlier work this paper cites.
Lee, C.-C., de Gyvez, J. P., 1996. Color image processing in a cellular neural-network environment. IEEE Transactions on Neural Networks 7 (5), 1086–1098
1996
Earlier work this paper cites.
Zamparelli, M., 1997. Genetically trained cellular neural networks. Neural Networks 10 (6), 1143–1151
1997
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11), 2278–2324
1998
Earlier work this paper cites.
de Ridder, D., Duin, R. P., Verbeek, P. W., Van Vliet, L., 1999. The applicability of neural networks to non-linear image processing. Pattern Analysis & Applications 2 (2), 111–128
1999
Earlier work this paper cites.
Franzen, R., 1999. Kodak lossless true color image suite. source: http://r0k. us/graphics/kodak 4
1999
Earlier work this paper cites.
Fan, E., 2000. Extended tanh-function method and its applications to nonlinear equations. Physics Letters A 277 (4-5), 212–218
2000
Earlier work this paper cites.
Hong, S.-W., Bao, P., 2000. An edge-preserving subband coding model based on non-adaptive and adaptive regularization. Image and Vision Computing 18 (8), 573–582
2000
Earlier work this paper cites.
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
Tamura, S., 1989. An analysis of a noise reduction neural network. In: International Conference on Acoustics, Speech, and Signal Processing,. IEEE, pp. 2001–2004
2004
Earlier work this paper cites.
Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W., 2005. An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation 4 (2), 460–489
2005
Earlier work this paper cites.
Roth, S., Black, M. J., 2005. Fields of experts: A framework for learning image priors. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Vol. 2. Citeseer, pp. 860–867
2005
Earlier work this paper cites.
2005
Earlier work this paper cites.
Aharon, M., Elad, M., Bruckstein, A., 2006. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54 (11), 4311–4322
2006
Earlier work this paper cites.
Elad, M., Aharon, M., 2006. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15 (12), 3736–3745
2006
Earlier work this paper cites.
Hinton, G., Osindero, S., ???? The, y. 2006. a fast learning algorithm for deep belief nets. Neural Computation 18 (7)
2006
Earlier work this paper cites.
Hinton, G. E., Salakhutdinov, R. R., 2006. Reducing the dimensionality of data with neural networks. Science 313 (5786), 504–507
2006
Earlier work this paper cites.
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., 2007. Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems. pp. 153–160
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
Marreiros, A. C., Daunizeau, J., Kiebel, S. J., Friston, K. J., 2008. Population dynamics: variance and the sigmoid activation function. Neuroimage 42 (1), 147–157
2008
Earlier work this paper cites.
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y., 2009. What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp. 2146–2153
2009
Earlier work this paper cites.
Mairal, J., Bach, F. R., Ponce, J., Sapiro, G., Zisserman, A., 2009. Non-local sparse models for image restoration. In: ICCV. Vol. 29. Citeseer, pp. 54–62
2009
Earlier work this paper cites.
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y., 2010. Theano: a cpu and gpu math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy). Vol. 4. Austin, TX
2010
Earlier work this paper cites.
Bottou, L., 2010. Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010. Springer, pp. 177–186
2010
Earlier work this paper cites.
Hore, A., Ziou, D., 2010. Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference on Pattern Recognition. IEEE, pp. 2366–2369
2010
Earlier work this paper cites.
Nair, V., Hinton, G. E., 2010. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10). pp. 807–814
2010
Earlier work this paper cites.
Stone, J. E., Gohara, D., Shi, G., 2010. Opencl: A parallel programming standard for heterogeneous computing systems. Computing in science & engineering 12 (3), 66
2010
Earlier work this paper cites.
Karlik, B., Olgac, A. V., 2011. Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems 1 (4), 111–122
2011
Earlier work this paper cites.
Nvidia, C., 2011. Nvidia cuda c programming guide. Nvidia Corporation 120 (18), 8
2011
Earlier work this paper cites.
Zoran, D., Weiss, Y., 2011. From learning models of natural image patches to whole image restoration. In: 2011 International Conference on Computer Vision. IEEE, pp. 479–486
2011
Earlier work this paper cites.
Burger, H. C., Schuler, C. J., Harmeling, S., 2012. Image denoising: Can plain neural networks compete with bm3d? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 2392–2399
2012
Earlier work this paper cites.
Dong, W., Zhang, L., Shi, G., Li, X., 2012. Nonlocally centralized sparse representation for image restoration. IEEE Transactions on Image Processing 22 (4), 1620–1630
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. pp. 1097–1105
2012
Earlier work this paper cites.
Yang, J., Zhang, L., Xu, Y., Yang, J.-y., 2012. Beyond sparsity: The role of l1-optimizer in pattern classification. Pattern Recognition 45 (3), 1104–1118
2012
Earlier work this paper cites.
Farooque, M. A., Rohankar, J. S., 2013. Survey on various noises and techniques for denoising the color image. International Journal of Application or Innovation in Engineering & Management (IJAIEM) 2 (11), 217–221
2013
Earlier work this paper cites.
Lin, M., Chen, Q., Yan, S., 2013. Network in network. arXiv preprint arXiv:1312.4400
2013
Earlier work this paper cites.
Yang, J., Chu, D., Zhang, L., Xu, Y., Yang, J., 2013. Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Transactions on Neural Networks and Learning Systems 24 (7), 1023–1035
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Gu, S., Zhang, L., Zuo, W., Feng, X., 2014. Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2862–2869
2014
Earlier work this paper cites.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T., 2014. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM, pp. 675–678
2014
Earlier work this paper cites.
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., Chen, M., 2014. Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, pp. 844–848
2014
Earlier work this paper cites.
Schmidt, U., Roth, S., 2014. Shrinkage fields for effective image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2774–2781
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Zuo, W., Zhang, L., Song, C., Zhang, D., Gao, H., 2014. Gradient histogram estimation and preservation for texture enhanced image denoising. IEEE Transactions on Image Processing 23 (6), 2459–2472
2014
Earlier work this paper cites.
Chollet, F., et al., 2015. Keras
2015
Earlier work this paper cites.
Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S. Z., Hospedales, T., 2015. When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: Proceedings of the IEEE international Conference on Computer Vision Workshops. pp. 142–150
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Lebrun, M., Colom, M., Morel, J.-M., 2015. The noise clinic: a blind image denoising algorithm. Image Processing On Line 5, 1–54
2015
Cited alongside, same era.
Liang, J., Liu, R., 2015. Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network. In: 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, pp. 697–701
2015
Cited alongside, same era.
Khoroushadi, M., Sadegh, M., 2018. Enhancement in low-dose computed tomography through image denoising techniques: Wavelets and deep learning. Ph.D. thesis, ProQuest Dissertations Publishing
2018
Later among the works it cites.
Latif, G., Iskandar, D. A., Alghazo, J., Butt, M., Khan, A. H., 2018. Deep cnn based mr image denoising for tumor segmentation using watershed transform. International Journal of Engineering & Technology 7 (2.3), 37–42
2018
Later among the works it cites.
Lee, D., Yun, S., Choi, S., Yoo, H., Yang, M.-H., Oh, S., 2018. Unsupervised holistic image generation from key local patches. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 19–35
2018
Later among the works it cites.
Li, L., Wu, J., Jin, X., 2018b. Cnn denoising for medical image based on wavelet domain. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, pp. 105–109
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural Networks 61, 85–117
2015
Cited alongside, same era.
Shantia, A., Timmers, R., Schomaker, L., Wiering, M., 2015. Indoor localization by denoising autoencoders and semi-supervised learning in 3d simulated environment. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–7
2015
Cited alongside, same era.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–9
2015
Cited alongside, same era.
Vedaldi, A., Lenc, K., 2015. Matconvnet: Convolutional neural networks for matlab. In: Proceedings of the 23rd ACM International Conference on Multimedia. ACM, pp. 689–692
2015
Cited alongside, same era.
Xu, Q., Zhang, C., Zhang, L., 2015b. Denoising convolutional neural network. In: 2015 IEEE International Conference on Information and Automation. IEEE, pp. 1184–1187
2015
Cited alongside, same era.
Zhang, Z., Wang, L., Kai, A., Yamada, T., Li, W., Iwahashi, M., 2015. Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification. EURASIP Journal on Audio, Speech, and Music Processing 2015 (1), 12
2015
Cited alongside, same era.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al., 2016. Tensorflow: A system for large-scale machine learning. In: 12th Symposium on Operating Systems Design and Implementation. pp. 265–283
2016
Cited alongside, same era.
Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A. K., 2018. Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Processing Magazine 35 (1), 20–36
2018
Later among the works it cites.
Ma, Y., Chen, X., Zhu, W., Cheng, X., Xiang, D., Shi, F., 2018. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cgan. Biomedical Optics Express 9 (11), 5129–5146
2018
Later among the works it cites.
Mafi, M., Martin, H., Cabrerizo, M., Andrian, J., Barreto, A., Adjouadi, M., 2018. A comprehensive survey on impulse and gaussian denoising filters for digital images. Signal Processing
2018
Later among the works it cites.
Majumdar, A., 2018. Blind denoising autoencoder. IEEE Transactions on Neural Networks and Learning Systems 30 (1), 312–317
2018
Later among the works it cites.
Mildenhall, B., Barron, J. T., Chen, J., Sharlet, D., Ng, R., Carroll, R., 2018. Burst denoising with kernel prediction networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2502–2510
2018
Later among the works it cites.
Panda, A., Naskar, R., Pal, S., 2018. Exponential linear unit dilated residual network for digital image denoising. Journal of Electronic Imaging 27 (5), 053024
2018
Later among the works it cites.
2018
Later among the works it cites.
Park, J. H., Kim, J. H., Cho, S. I., 2018. The analysis of cnn structure for image denoising. In: 2018 International SoC Design Conference (ISOCC). IEEE, pp. 220–221
2018
Later among the works it cites.
Remez, T., Litany, O., Giryes, R., Bronstein, A. M., 2018. Class-aware fully convolutional gaussian and poisson denoising. IEEE Transactions on Image Processing 27 (11), 5707–5722
2018
Later among the works it cites.
Sadda, P., Qarni, T., 2018. Real-time medical video denoising with deep learning: application to angiography. International journal of applied information systems 12 (13), 22
2018
Later among the works it cites.
Sheremet, O., Sheremet, K., Sadovoi, O., Sokhina, Y., 2018. Convolutional neural networks for image denoising in infocommunication systems. In: 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). IEEE, pp. 429–432
2018
Later among the works it cites.
Soltanayev, S., Chun, S. Y., 2018. Training deep learning based denoisers without ground truth data. In: Advances in Neural Information Processing Systems. pp. 3257–3267
2018
Later among the works it cites.
Sun, X., Kottayil, N. K., Mukherjee, S., Cheng, I., 2018. Adversarial training for dual-stage image denoising enhanced with feature matching. In: International Conference on Smart Multimedia. Springer, pp. 357–366
2018
Later among the works it cites.
Tian, C., Xu, Y., Fei, L., Yan, K., 2018. Deep learning for image denoising: a survey. In: International Conference on Genetic and Evolutionary Computing. Springer, pp. 563–572
2018
Later among the works it cites.
2018
Later among the works it cites.
Uchida, K., Tanaka, M., Okutomi, M., 2018. Non-blind image restoration based on convolutional neural network. In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE, pp. 40–44
2018
Later among the works it cites.
Wang, H., Wang, Q., Gao, M., Li, P., Zuo, W., 2018. Multi-scale location-aware kernel representation for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1248–1257
2018
Later among the works it cites.
Xiao, P., Guo, Y., Zhuang, P., 2018. Removing stripe noise from infrared cloud images via deep convolutional networks. IEEE Photonics Journal 10 (4), 1–14
2018
Later among the works it cites.
Xie, W., Li, Y., Jia, X., 2018. Deep convolutional networks with residual learning for accurate spectral-spatial denoising. Neurocomputing 312, 372–381
2018
Later among the works it cites.
Yao, Y., Wu, X., Zhang, L., Shan, S., Zuo, W., 2018. Joint representation and truncated inference learning for correlation filter based tracking. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 552–567
2018
Later among the works it cites.
Ye, J. C., Han, Y., Cha, E., 2018. Deep convolutional framelets: A general deep learning framework for inverse problems. SIAM Journal on Imaging Sciences 11 (2), 991–1048
2018
Later among the works it cites.
Yeh, R. A., Lim, T. Y., Chen, C., Schwing, A. G., Hasegawa-Johnson, M., Do, M., 2018. Image restoration with deep generative models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6772–6776
2018
Later among the works it cites.
Yu, A., Liu, X., Wei, X., Fu, T., Liu, D., 2018. Generative adversarial networks with dense connection for optical coherence tomography images denoising. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, pp. 1–5
2018
Later among the works it cites.
Zarshenas, A., Suzuki, K., 2018. Deep neural network convolution for natural image denoising. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 2534–2539
2018
Later among the works it cites.
2018
Later among the works it cites.
Zhang, F., Liu, D., Wang, X., Chen, W., Wang, W., 2018a. Random noise attenuation method for seismic data based on deep residual networks. In: International Geophysical Conference, Beijing, China, 24-27 April 2018. Society of Exploration Geophysicists and Chinese Petroleum Society, pp. 1774–1777
2018
Later among the works it cites.
Zhang, J., Ghanem, B., 2018. Ista-net: Interpretable optimization-inspired deep network for image compressive sensing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1828–1837
2018
Later among the works it cites.
ZhiPing, Q., YuanQi, Z., Yi, S., XiangBo, L., 2018. A new generative adversarial network for texture preserving image denoising. In: 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, pp. 1–5
2018
Later among the works it cites.
Abbasi, A., Monadjemi, A., Fang, L., Rabbani, H., Zhang, Y., 2019. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks. Computers in Biology and Medicine 108, 1–8
2019
Closest in time.
Abiko, R., Ikehara, M., 2019. Blind denoising of mixed gaussian-impulse noise by single cnn. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1717–1721
2019
Closest in time.
Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., Barron, J. T., 2019. Unprocessing images for learned raw denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11036–11045
2019
Closest in time.
Chen, Y., Yu, M., Jiang, G., Peng, Z., Chen, F., 2019. End-to-end single image enhancement based on a dual network cascade model. Journal of Visual Communication and Image Representation 61, 284–295
2019
Closest in time.
Choi, K., Vania, M., Kim, S., 2019. Semi-supervised learning for low-dose ct image restoration with hierarchical deep generative adversarial network (hd-gan). In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2683–2686
2019
Closest in time.
Cui, J., Gong, K., Guo, N., Wu, C., Meng, X., Kim, K., Zheng, K., Wu, Z., Fu, L., Xu, B., et al., 2019. Pet image denoising using unsupervised deep learning. European journal of nuclear medicine and molecular imaging 46 (13), 2780–2789
2019
Closest in time.
Du, B., Wei, Q., Liu, R., 2019. An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
Ehret, T., Davy, A., Morel, J.-M., Facciolo, G., Arias, P., 2019. Model-blind video denoising via frame-to-frame training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11369–11378
2019
Closest in time.
Fu, B., Zhao, X., Li, Y., Wang, X., Ren, Y., 2019. A convolutional neural networks denoising approach for salt and pepper noise. Multimedia Tools and Applications 78 (21), 30707–30721
2019
Closest in time.
Gardner, E., Wallace, D., Stroud, N., 1989. Training with noise and the storage of correlated patterns in a neural network model. Journal of Physics A: Mathematical and General 22 (12), 2019
2019
Closest in time.
Guan, J., Lai, R., Xiong, A., 2019. Wavelet deep neural network for stripe noise removal. IEEE Access 7, 44544–44554
2019
Closest in time.
Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L., 2019. Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1712–1722
2019
Closest in time.
Jia, X., Liu, S., Feng, X., Zhang, L., 2019. Focnet: A fractional optimal control network for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6054–6063
2019
Closest in time.
Jifara, W., Jiang, F., Rho, S., Cheng, M., Liu, S., 2019. Medical image denoising using convolutional neural network: a residual learning approach. The Journal of Supercomputing 75 (2), 704–718
2019
Closest in time.
Khan, S., Khan, K. S., Shin, S. Y., 2019. Symbol denoising in high order m-qam using residual learning of deep cnn. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, pp. 1–6
2019
Closest in time.
Kokkinos, F., Lefkimmiatis, S., 2019. Iterative joint image demosaicking and denoising using a residual denoising network. IEEE Transactions on Image Processing
2019
Closest in time.
Li, Z., Wu, J., 2019. Learning deep cnn denoiser priors for depth image inpainting. Applied Sciences 9 (6), 1103
2019
Closest in time.
Liang, X., Zhang, D., Lu, G., Guo, Z., Luo, N., 2019. A novel multicamera system for high-speed touchless palm recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems
2019
Closest in time.
Lin, K., Li, T. H., Liu, S., Li, G., 2019. Real photographs denoising with noise domain adaptation and attentive generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 0–0
2019
Closest in time.
Liu, W., Lee, J., 2019. A 3-d atrous convolution neural network for hyperspectral image denoising. IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
Liu, X., Suganuma, M., Sun, Z., Okatani, T., 2019. Dual residual networks leveraging the potential of paired operations for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7007–7016
2019
Closest in time.
Lu, Y., Wong, W., Lai, Z., Li, X., 2019. Robust flexible preserving embedding. IEEE Transactions on Cybernetics
2019
Closest in time.
Marinč, T., Srinivasan, V., Gül, S., Hellge, C., Samek, W., 2019. Multi-kernel prediction networks for denoising of burst images. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 2404–2408
2019
Closest in time.
Peng, Y., Zhang, L., Liu, S., Wu, X., Zhang, Y., Wang, X., 2019. Dilated residual networks with symmetric skip connection for image denoising. Neurocomputing 345, 67–76
2019
Closest in time.
Priyanka, S. A., Wang, Y.-K., 2019. Fully symmetric convolutional network for effective image denoising. Applied Sciences 9 (4), 778
2019
Closest in time.
Ran, M., Hu, J., Chen, Y., Chen, H., Sun, H., Zhou, J., Zhang, Y., 2019. Denoising of 3d magnetic resonance images using a residual encoder–decoder wasserstein generative adversarial network. Medical Image Analysis 55, 165–180
2019
Closest in time.
Song, Y., Zhu, Y., Du, X., 2019. Dynamic residual dense network for image denoising. Sensors 19 (17), 3809
2019
Closest in time.
Su, Y., Lian, Q., Zhang, X., Shi, B., Fan, X., 2019. Multi-scale cross-path concatenation residual network for poisson denoising. IET Image Processing
2019
Closest in time.
Tan, H., Xiao, H., Lai, S., Liu, Y., Zhang, M., 2019. Deep residual learning for burst denoising. In: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, pp. 156–161
2019
Closest in time.
Tassano, M., Delon, J., Veit, T., 2019b. Dvdnet: A fast network for deep video denoising. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1805–1809
2019
Closest in time.
Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J., Luo, N., 2019. Enhanced cnn for image denoising. CAAI Transactions on Intelligence Technology 4 (1), 17–23
2019
Closest in time.
Wang, X., Dai, F., Ma, Y., Guo, J., Zhao, Q., Zhang, Y., 2019. Near-infrared image guided neural networks for color image denoising. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 3807–3811
2019
Closest in time.
Wei, J., Xia, Y., Zhang, Y., 2019. M3net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation. Pattern Recognition 91, 366–378
2019
Closest in time.
Wu, S., Xu, Y., 2019. Dsn: A new deformable subnetwork for object detection. IEEE Transactions on Circuits and Systems for Video Technology
2019
Closest in time.
Xiao, X., Xiong, N. N., Lai, J., Wang, C.-D., Sun, Z., Yan, J., 2019. A local consensus index scheme for random-valued impulse noise detection systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems
2019
Closest in time.
Yu, S., Ma, J., Wang, W., 2019. Deep learning for denoising. Geophysics 84 (6), V333–V350
2019
Closest in time.
Yue, Z., Yong, H., Zhao, Q., Meng, D., Zhang, L., 2019. Variational denoising network: Toward blind noise modeling and removal. In: Advances in Neural Information Processing Systems. pp. 1688–1699
2019
Closest in time.
Zheng, Y., Duan, H., Tang, X., Wang, C., Zhou, J., 2019. Denoising in the dark: Privacy-preserving deep neural network based image denoising. IEEE Transactions on Dependable and Secure Computing
2019
Closest in time.
Broaddus, C., Krull, A., Weigert, M., Schmidt, U., Myers, G., 2020. Removing structured noise with self-supervised blind-spot networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, pp. 159–163
2020
Closest in time.
Meng, M., Li, S., Yao, L., Li, D., Zhu, M., Gao, Q., Xie, Q., Zhao, Q., Bian, Z., Huang, J., et al., 2020. Semi-supervised learned sinogram restoration network for low-dose ct image reconstruction. In: Medical Imaging 2020: Physics of Medical Imaging. Vol. 11312. International Society for Optics and Photonics, p. 113120B
2020
Closest in time.
Sivakumar, K., Desai, U. B., 1993. Image restoration using a multilayer perceptron with a multilevel sigmoidal function. IEEE Transactions on Signal Processing 41 (5), 2018–2022
2022
Closest in time.
Zhang, B., Jin, S., Xia, Y., Huang, Y., Xiong, Z., 2020. Attention mechanism enhanced kernel prediction networks for denoising of burst images. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 2083–2087
2087
Closest in time.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., 2007. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Transactions on Image Processing 16 (8), 2080–2095
2095
Closest in time.