5.3.8 Neural Networks for Noise Removal, Denoising

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Neural Networks. Noise Removal. Denoising.

Zhou, Y.T., Chellappa, R., and Jenkins, B.K.,
Image Restoration Using a Neural Network,
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Figueiredo, M.A.T., Leitao, J.M.N.,
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IP(3), No. 6, November 1994, pp. 789-801.
IEEE DOI 0402
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And:
Adaptive discontinuity location in image restoration,
ICIP94(II: 665-669).
IEEE DOI 9411
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Wong, H.S.[Hau-San], Guan, L.[Ling],
Adaptive Regularization in Image Restoration Using a Model Based Neural Network,
OptEng(36), No. 12, December 1997, pp. 3297-3308. 9801
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Wong, H.S.[Hau-San], Guan, L.[Ling],
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And:
A Fuzzy Model-based Neural Network for Adaptive Regularization in Image Restoration,
ICIP99(I:391-395).
IEEE DOI BibRef

Leitao, J.M.N., Figueiredo, M.A.T.,
Absolute Phase Image-Reconstruction: A Stochastic Nonlinear Filtering Approach,
IP(7), No. 6, June 1998, pp. 868-882.
IEEE DOI 9806
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Wang, Y., Wahl, F.M.,
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VISP(144), No. 4, August 1997, pp. 233-236. 9806
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Sun, Y.[Yi], Li, J.G.[Jie-Gu], Yu, S.Y.[Song-Yu],
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IEEE DOI 0402
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Wang, Y.M.[Yuan-Mei],
Neural Network Approach to Image Reconstruction from Projections,
IJIST(9), No. 5, 1999, pp. 381-387. BibRef 9900

Woo, W.L., Khor, L.C.,
Blind restoration of nonlinearly mixed signals using multilayer polynomial neural network,
VISP(151), No. 1, February 2004, pp. 51-61.
IEEE Abstract. 0403
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Woo, W.L., Dlay, S.S.,
Regularised nonlinear blind signal separation using sparsely connected network,
VISP(152), No. 1, February 2005, pp. 61-73.
IEEE Abstract. 0501
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Woo, W.L., Dlay, S.S.,
Nonlinear blind source separation using a hybrid RBF-FMLP network,
VISP(152), No. 2, April 2005, pp. 173-183.
DOI Link 0510
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Khor, L.C., Woo, W.L., Dlay, S.S.,
Nonlinear blind signal separation with intelligent controlled learning,
VISP(152), No. 3, June 2005, pp. 297-306.
DOI Link 0510
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Wei, C., Woo, W.L., Dlay, S.S., Khor, L.C.,
Maximum a posteriori-based approach to blind nonlinear underdetermined mixture,
VISP(153), No. 4, August 2006, pp. 419-430.
WWW Link. 0705
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Bao, P.[Paul], Wang, D.H.[Dian-Hui],
An Edge-Preserving Image Reconstruction Using Neural Network,
JMIV(14), No. 2, March 2001, pp. 117-130.
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Wang, Y.Q.[Yi-Qing], Morel, J.M.[Jean-Michel],
Can a Single Image Denoising Neural Network Handle All Levels of Gaussian Noise?,
SPLetters(21), No. 9, Sept 2014, pp. 1150-1153.
IEEE DOI 1406
See also SURE Guided Gaussian Mixture Image Denoising. Gaussian noise BibRef

Wang, Y.Q.[Yi-Qing],
A Note on the Size of Denoising Neural Networks,
SIIMS(9), No. 1, 2016, pp. 275-286.
DOI Link 1604
BibRef

Wang, Y.Q.[Yi-Qing],
Small Neural Networks can Denoise Image Textures Well: A Useful Complement to BM3D,
IPOL(6), 2016, pp. 1-7.
DOI Link 1601
See also Image denoising: Can plain neural networks compete with BM3D?. See also Analysis and Implementation of the BM3D Image Denoising Method, Image Processing, An. See also Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing, A. BibRef

Lyu, G.[Guohao], Yin, H.[Hui], Yu, X.[Xinyan], Luo, S.W.[Si-Wei],
A Local Characteristic Image Restoration Based on Convolutional Neural Network,
IEICE(E99-D), No. 8, August 2016, pp. 2190-2193.
WWW Link. 1608
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Zhang, K.[Kai], Zuo, W.M.[Wang-Meng], Chen, Y.J.[Yun-Jin], Meng, D.Y.[De-Yu], Zhang, L.[Lei],
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,
IP(26), No. 7, July 2017, pp. 3142-3155.
IEEE DOI 1706
Computational modeling, Image denoising, Neural networks, Noise level, Noise reduction, Training, Transform coding, Image denoising, batch normalization, convolutional neural networks, residual, learning BibRef


Jiao, J., Tu, W.C., He, S., Lau, R.W.H.[Rynson W.H.],
FormResNet: Formatted Residual Learning for Image Restoration,
NTIRE17(1034-1042)
IEEE DOI 1709
Image reconstruction, Image resolution, Image restoration, Neural networks, Noise reduction, Training, Visualization BibRef

Chaudhury, S., Roy, H.,
Can fully convolutional networks perform well for general image restoration problems?,
MVA17(254-257)
DOI Link 1708
Convolution, Image denoising, Image reconstruction, Image restoration, Image segmentation, Noise measurement, Training BibRef

Divakar, N., Babu, R.V.,
Image Denoising via CNNs: An Adversarial Approach,
NTIRE17(1076-1083)
IEEE DOI 1709
Feature extraction, Generators, Image denoising, Image reconstruction, Noise measurement, Noise reduction, Training BibRef

Koziarski, M.[Michal], Cyganek, B.[Boguslaw],
Deep Neural Image Denoising,
ICCVG16(163-173).
Springer DOI 1611
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Mejía-Lavalle, M.[Manuel], Ortiz, E.[Estela], Mújica, D.[Dante], Ruiz, J.[José], Reyes, G.[Gerardo],
An Effective Image De-noising Alternative Approach Based on Third Generation Neural Networks,
MCPR16(64-73).
Springer DOI 1608
BibRef

Jiang, M.Y.[Ming-Yong], Chen, X.N.[Xiang-Ning], Yu, X.Q.[Xia-Qiong],
Adaptive Sub-Optimal Hopfield Neural Network image restoration base on edge detection,
IASP11(364-367).
IEEE DOI 1112
BibRef

Bernues, E., Cisneros, G., Capella, M.,
Truncated edges estimation using MLP neural nets applied to regularized image restoration,
ICIP02(I: 341-344).
IEEE DOI 0210
BibRef

Chen, Z.Y.[Zhong-Yu], Desai, M.,
Multiple-valued feedback neural networks for image restoration,
ICIP96(I: 753-756).
IEEE DOI 9610
BibRef

Beaudot, W.H.A.[William H.A.],
Adaptive Spatiotemporal Filtering by a Neuromorphic Model of the Vertebrate Retina,
ICIP96(I: 427-430).
IEEE DOI BibRef 9600

Stajniak, A., Szostakowski, J.,
Neural implementation of ARMA type filters for image restoration,
ICIP95(II: 520-522).
IEEE DOI 9510
BibRef

Tan, B.H.[Beng-Heok], Wahah, A., Tan, E.C.[Eng-Chong],
A neural approach to optical image reconstruction,
ICIP95(II: 531-534).
IEEE DOI 9510
BibRef

Muneyasu, M., Yamamoto, K., Hinamoto, T.,
Image restoration using layered neural networks and Hopfield networks,
ICIP95(II: 33-36).
IEEE DOI 9510
BibRef

Swiniarski, R., Butler, M.P.,
Neural recurrent estimator to gray scale image restoration based on 2D Kalman filtering,
ICPR92(III:425).
IEEE DOI 9208
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Noise Removal, Denoising .


Last update:Sep 25, 2017 at 16:36:46