Open AccessAnisotropy‐based image smoothing via deep neural network training
Author(s) -
Chen Qun,
Liu Bozhi,
Zhou Fei
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.2263
Subject(s) - smoothing , anisotropy , artificial intelligence , pixel , computer vision , image (mathematics) , edge preserving smoothing , computer science , artificial neural network , enhanced data rates for gsm evolution , anisotropic diffusion , pattern recognition (psychology) , optics , physics
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.