NDLI: Anisotropy-based image smoothing via deep neural network training

Content Provider	IET Digital Library
Author	Chen, Qun Liu, Bozhi Zhou, Fei
Abstract	An anisotropy-based image smoothing method is proposed to remove image details from various images. Image details appear as the textures in the image. After removing them, the remaining part is known as image structure. To effectively distinguish image structures and textures, the authors present an anisotropy-based measurement which depicts the anisotropy degree of local gradients for each edge pixel. The pixels with larger anisotropy are more likely to be the ones on structural edges. Then, the anisotropy-based measurement is embedded in a regularised objective function. To achieve the image smoothing, the objective function is finally optimised by training a deep network. Visual results demonstrate that the proposed method is powerful to keep the edges in the smoothed images sharp and eliminate trivial details simultaneously.
Starting Page	1279
Ending Page	1281
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 24, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/24
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.2263
Journal	Electronics Letters
Publisher Date	2019-10-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Anisotropy Degree Anisotropy-based Image Smoothing Method Anisotropy-based Measurement Computer Vision And Image Processing Technique Deep Network Deep Neural Network Training Edge Detection Edge Pixel Image Denoising Image Details Image Enhancement Image Recognition Image Structure Image Texture Larger Anisotropy Neural Nets Optical, Image And Video Signal Processing Optimisation Smoothed Image Smoothing Method Statistics Structural Edge
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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