Image Filtering With Generic Geometric Prior

This paper first presents a generic geometric prior for the image processing problems. The proposed term allows each individual pixel to automatically choose its own geometric prior. This behavior is fundamentally different from traditional regularizations that use only one prior for all pixels. Thi...

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Bibliographic Details
Published inIEEE access Vol. 6; pp. 54320 - 54330
Main Authors Gong, Yuanhao, Hou, Xianxu, Li, Fei, Qiu, Guoping
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Filter
half window
Image edge detection
Image filters
Image processing
Iterative methods
Kernel
Microsoft Windows
Neural networks
Optimization
Pixels
prior
regularization
smoothing
Smoothing methods
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Summary:This paper first presents a generic geometric prior for the image processing problems. The proposed term allows each individual pixel to automatically choose its own geometric prior. This behavior is fundamentally different from traditional regularizations that use only one prior for all pixels. This term, however, is difficult to be minimized by traditional optimization methods. Therefore, we further propose an iterative image filter to impose this generic geometric prior. Moreover, this proposed filter has a neural network representation, where the kernels in our filter can be learned based on the convolutional neural network. Several numerical experiments are performed to confirm the effectiveness and efficiency of this new filter and its related neural networks.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2871829