A Noise-Robust Online convolutional coding model and its applications to poisson denoising and image fusion

In this paper, we propose a noise-robust online convolutional coding model for image representation, which can use the noisy images as training data. Then an alternating algorithm is utilized to convert the model into two sub-problems, the image pursuit problem and the dictionary learning problem. F...

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Vydáno v:Applied Mathematical Modelling Ročník 95; s. 644
Hlavní autoři: Wang, Wei, Xiang-Xia, He, Chuanjiang, Ren, Zemin, Wang, Tianfu, Lei, Baiying
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier BV 01.07.2021
Témata:
Algorithms
Computer vision
Dictionaries
Euler-Lagrange equation
Fourier transforms
Gaussian elimination
Image coding
Image processing
Learning
Noise
Noise reduction
Representations
Robustness
Training
ISSN:1088-8691, 0307-904X
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Shrnutí:In this paper, we propose a noise-robust online convolutional coding model for image representation, which can use the noisy images as training data. Then an alternating algorithm is utilized to convert the model into two sub-problems, the image pursuit problem and the dictionary learning problem. For the image pursuit problem, the Gauss elimination method is used to solve the equation set which is derived by the Euler equation and discrete Fourier transform. For the dictionary learning problem, a gradient-descent flow is derived to solve it. Experimental results show that our method can output more meaningful feature representations compared to the related models while the training data was corrupted by Poisson noise.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2021.02.023