*Zhou, Y.T.*,
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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
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**Adaptive discontinuity location in image restoration**,

*ICIP94*(II: 665-669).

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

**Adaptive Regularization in Image Restoration Using a
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*Wong, H.S.[Hau-San]*,
*Guan, L.[Ling]*,

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*JEI(7)*, No. 1, January 1998, pp. 211-221.
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**A Fuzzy Model-based Neural Network for Adaptive Regularization in Image
Restoration**,

*ICIP99*(I:391-395).

IEEE DOI
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*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.

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*Wang, Y.*,
*Wahl, F.M.*,

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*VISP(144)*, No. 4, August 1997, pp. 233-236.
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*Sun, Y.[Yi]*,
*Li, J.G.[Jie-Gu]*,
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**Improvement on performance of modified Hopfield neural network for
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*IP(4)*, No. 5, May 1995, pp. 688-692.

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*Wang, Y.M.[Yuan-Mei]*,

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*Woo, W.L.*,
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*Woo, W.L.*,
*Dlay, S.S.*,

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*Woo, W.L.*,
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**Nonlinear blind source separation using a hybrid RBF-FMLP network**,

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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.*,
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**Maximum a posteriori-based approach to blind nonlinear underdetermined
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*JMIV(14)*, No. 2, March 2001, pp. 117-130.

DOI Link
**0106**

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*Wang, Y.Q.[Yi-Qing]*,
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**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
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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.

DOI Link
**1604**

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*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.
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*Lyu, G.[Guohao]*,
*Yin, H.[Hui]*,
*Yu, X.[Xinyan]*,
*Luo, S.W.[Si-Wei]*,

**A Local Characteristic Image Restoration Based on Convolutional Neural
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*IEICE(E99-D)*, No. 8, August 2016, pp. 2190-2193.

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**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

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

**FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image
Denoising**,

*IP(27)*, No. 9, September 2018, pp. 4608-4622.

IEEE DOI
**1807**

image denoising, image sampling,
learning (artificial intelligence), neural nets,
spatially variant noise
See also Analysis and Implementation of the FFDNet Image Denoising Method, An.
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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,
Noise reduction, Optimization, methods
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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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*Yin, J.*,
*Chen, B.*,
*Li, Y.*,

**Highly Accurate Image Reconstruction for Multimodal Noise Suppression
Using Semisupervised Learning on Big Data**,

*MultMed(20)*, No. 11, November 2018, pp. 3045-3056.

IEEE DOI
**1810**

Image reconstruction, Noise measurement, Streaming media,
Cost function, Semisupervised learning, Big Data, Imaging,
semisupervised learning
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*Xu, S.*,
*Liu, T.*,
*Zhang, G.*,
*Tang, Y.*,

**A Two-Stage Noise Level Estimation Using Automatic Feature Extraction
and Mapping Model**,

*SPLetters(26)*, No. 1, January 2019, pp. 179-183.

IEEE DOI
**1901**

convolution, feature extraction, feedforward neural nets,
optical distortion, two-stage noise level estimation algorithm,
mapping model
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*Tassano, M.[Matias]*,
*Delon, J.[Julie]*,
*Veit, T.[Thomas]*,

**An Analysis and Implementation of the FFDNet Image Denoising Method**,

*IPOL(9)*, 2019, pp. 1-25.

DOI Link
**1901**

*Code, Noise Removal*.
See also FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising.
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*Xu, W.J.[Wen-Jia]*,
*Xu, G.L.[Guang-Luan]*,
*Wang, Y.[Yang]*,
*Sun, X.[Xian]*,
*Lin, D.[Daoyu]*,
*Wu, Y.R.[Yi-Rong]*,

**Deep Memory Connected Neural Network for Optical Remote Sensing Image
Restoration**,

*RS(10)*, No. 12, 2018, pp. xx-yy.

DOI Link
**1901**

BibRef

*Cho, S.I.*,
*Kang, S.*,

**Gradient Prior-Aided CNN Denoiser With Separable Convolution-Based
Optimization of Feature Dimension**,

*MultMed(21)*, No. 2, February 2019, pp. 484-493.

IEEE DOI
**1902**

Convolution, Noise reduction, Image denoising, Feature extraction,
Training, Noise measurement, Indexes, Image denoising,
image noise
BibRef

*Xue, H.Z.[Hong-Zhi]*,
*Cui, H.W.[Hong-Wei]*,

**Research on image restoration algorithms based on BP neural network**,

*JVCIR(59)*, 2019, pp. 204-209.

Elsevier DOI
**1903**

Image restoration, Image processing, Image denoising, BP neural network
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*Shi, W.Z.[Wu-Zhen]*,
*Jiang, F.[Feng]*,
*Zhang, S.P.[Sheng-Ping]*,
*Wang, R.[Rui]*,
*Zhao, D.B.[De-Bin]*,
*Zhou, H.Y.[Hui-Yu]*,

**Hierarchical residual learning for image denoising**,

*SP:IC(76)*, 2019, pp. 243-251.

Elsevier DOI
**1906**

Image denoising, Convolutional neural network,
Residual learning, Hierarchical residual learning, Multi-scale information
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*Shin, S.Y.[Soo-Yeon]*,
*Kim, D.M.[Dong-Myung]*,
*Suh, J.W.[Jae-Won]*,

**Image Denoiser Using Convolutional Neural Network with Deconvolution
and Modified Residual Network**,

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**1908**

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Springer DOI

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*Yu, K.*,
*Dong, C.*,
*Lin, L.*,
*Loy, C.C.*,

**Crafting a Toolchain for Image Restoration by Deep Reinforcement
Learning**,

*CVPR18*(2443-2452)

IEEE DOI
**1812**

Image restoration, Tools, Distortion, Task analysis,
Transform coding, Complexity theory
BibRef

*Lefkimmiatis, S.*,

**Universal Denoising Networks:
A Novel CNN Architecture for Image Denoising**,

*CVPR18*(3204-3213)

IEEE DOI
**1812**

Image restoration, Noise level, Distortion, Image denoising,
Training, Noise reduction, Transforms
BibRef

*Ryu, J.*,
*Kim, Y.*,

**Conditional Distribution Learning with Neural Networks and its
Application to Universal Image Denoising**,

*ICIP18*(3214-3218)

IEEE DOI
**1809**

Neural networks, Noise reduction, Noise measurement,
Context modeling, Gray-scale, Boats, Training, Universal denoising,
plug-in approach
BibRef

*Song, P.*,
*Rodrigues, M.R.D.*,

**Multimodal Image Denoising Based on Coupled Dictionary Learning**,

*ICIP18*(515-519)

IEEE DOI
**1809**

Dictionaries, Image denoising, Machine learning, Training,
Noise reduction, Noise measurement, Image reconstruction,
guidance information
BibRef

*Somasundaran, B.V.*,
*Soundararajan, R.*,
*Biswas, S.*,

**Image Denoising for Image Retrieval by Cascading a Deep Quality
Assessment Network**,

*ICIP18*(525-529)

IEEE DOI
**1809**

Noise reduction, Image retrieval, Image denoising, Image quality,
Noise measurement, Training, Image quality assessment,
image retrieval
BibRef

*Li, Y.*,
*Zhang, B.*,
*Florent, R.*,

**Understanding neural-network denoisers through an activation function
perspective**,

*ICIP17*(2971-2975)

IEEE DOI
**1803**

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**

BibRef

*Gao, R.*,
*Grauman, K.[Kristen]*,

**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
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**

BibRef

*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:Aug 20, 2019 at 20:38:45