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

Wei Wang*, Xiang Gen Xia, Chuanjiang He, Zemin Ren, Tianfu Wang, Baiying Lei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)644-666
Number of pages23
JournalApplied Mathematical Modelling
Volume95
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

Keywords

  • Convolutional coding
  • Convolutional dictionary learning
  • Gradient descent flow
  • Online dictionary learning

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Wang, W., Xia, X. G., He, C., Ren, Z., Wang, T., & Lei, B. (2021). A Noise-Robust Online convolutional coding model and its applications to poisson denoising and image fusion. Applied Mathematical Modelling, 95, 644-666. https://doi.org/10.1016/j.apm.2021.02.023