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*IP(3)*, No. 6, November 1994, pp. 789-801.

IEEE DOI
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*ICIP94*(II: 665-669).

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*Wong, H.S.[Hau-San]*,
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*Wong, H.S.[Hau-San]*,
*Guan, L.[Ling]*,

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*ICIP99*(I:391-395).

IEEE DOI
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*Leitao, J.M.N.*,
*Figueiredo, M.A.T.*,

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*IP(7)*, No. 6, June 1998, pp. 868-882.

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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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*Woo, W.L.*,
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*Khor, L.C.*,
*Woo, W.L.*,
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**Nonlinear blind signal separation with intelligent controlled learning**,

*VISP(152)*, No. 3, June 2005, pp. 297-306.

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*Wei, C.*,
*Woo, W.L.*,
*Dlay, S.S.*,
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**Can a Single Image Denoising Neural Network Handle All Levels of
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See also SURE Guided Gaussian Mixture Image Denoising. Gaussian noise
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*Wang, Y.Q.[Yi-Qing]*,

**A Note on the Size of Denoising Neural Networks**,

*SIIMS(9)*, No. 1, 2016, pp. 275-286.

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**Small Neural Networks can Denoise Image Textures Well:
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**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.
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*Yin, H.[Hui]*,
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*Zhang, K.[Kai]*,
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*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

*Zhang, K.[Kai]*,
*Zuo, W.M.[Wang-Meng]*,
*Zhang, L.[Lei]*,

**FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image
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*IP(27)*, No. 9, September 2018, pp. 4608-4622.

IEEE DOI
**1807**

image denoising, image sampling,
learning (artificial intelligence), neural nets,
spatially variant noise
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*Zhang, K.[Kai]*,
*Zuo, W.M.[Wang-Meng]*,
*Gu, S.*,
*Zhang, L.[Lei]*,

**Learning Deep CNN Denoiser Prior for Image Restoration**,

*CVPR17*(2808-2817)

IEEE DOI
**1711**

Image restoration, Inverse problems, Learning systems,
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*Zhang, F.[Fu]*,
*Cai, N.[Nian]*,
*Wu, J.X.[Ji-Xiu]*,
*Cen, G.D.[Guan-Dong]*,
*Wang, H.[Han]*,
*Chen, X.D.[Xin-Du]*,

**Image denoising method based on a deep convolution neural network**,

*IET-IPR(12)*, No. 4, April 2018, pp. 485-493.

DOI Link
**1804**

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IEEE DOI

Biological neural networks, Image denoising, Kernel, Noise measurement, Noise reduction, Training, Activation function, Neural network denoising BibRef

*Li, J.J.[Jian-Jun]*,
*Xu, L.L.[Lan-Lan]*,
*Li, H.J.[Hao-Jie]*,
*Chang, C.C.[Chin-Chen]*,
*Sun, F.M.[Fu-Ming]*,

**Parameter Selection for Denoising Algorithms Using NR-IQA with CNN**,

*MMMod18*(I:381-392).

Springer DOI
**1802**

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*Gao, R.*,
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**On-demand Learning for Deep Image Restoration**,

*ICCV17*(1095-1104)

IEEE DOI
**1802**

convolution, image denoising, image restoration, interpolation,
learning (artificial intelligence), neural nets,
Training
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
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*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
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*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
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*Cyganek, B.[Boguslaw]*,

**Deep Neural Image Denoising**,

*ICCVG16*(163-173).

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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
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*MCPR16*(64-73).

Springer DOI
**1608**

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**Adaptive Sub-Optimal Hopfield Neural Network image restoration base on
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*IASP11*(364-367).

IEEE DOI
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**Truncated edges estimation using MLP neural nets applied to regularized
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*ICIP02*(I: 341-344).

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**0210**

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*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
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**9600**

*Stajniak, A.*,
*Szostakowski, J.*,

**Neural implementation of ARMA type filters for image restoration**,

*ICIP95*(II: 520-522).

IEEE DOI
**9510**

BibRef

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*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:Aug 16, 2018 at 18:22:30