NDLI: Image denoising method based on a deep convolution neural network

Content Provider	IET Digital Library
Author	Zhang, Fu Cai, Nian Wu, Jixiu Cen, Guandong Wang, Han Chen, Xindu
Abstract	Image denoising is still a challenging problem in image processing. The authors propose a novel image denoising method based on a deep convolution neural network (DCNN). Different from other learning-based methods, the authors design a DCNN to achieve the noise image. Thus, the latent clear image can be achieved by separating the noise image from the contaminated image. At the training stage, the gradient clipping scheme is employed to prevent gradient explosions and enables the network to converge quickly. Experimental results demonstrate that the proposed denoising method can achieve a better performance compared with the state-of-the-art denoising methods. Also, the results indicate that the denoising method has the ability of suppressing different noises with different noise levels by means of one single denoising model.
Starting Page	485
Ending Page	493
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 4, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0389
Journal	IET Image Processing
Publisher Date	2017-11-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Contaminated Image Convergence of Numerical Method DCNN Deep Convolution Neural Network Feedforward Neural Network Gradient Clipping Scheme Gradient Method Image Denoising Image Denoising Method Interpolation And Function Approximation Latent Clear Image Neural Computing Technique Noise Levels Numerical Analysis Optical, Image And Video Signal Processing Single Denoising Model Training Stage
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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