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. 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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a noise-robust online convolutional coding model for image representation, which employs an alternating algorithm to tackle the image pursuit and dictionary learning problems. Experimental results demonstrate that the proposed method can generate more meaningful feature representations compared to existing models when the training data is corrupted by Poisson noise.