Abstract
In the paper, a novel approach to noise removing bases on Variational Mode Decomposition and Total Variation Denoising is presented. Variational Mode Decomposition is a state-of-the-art adaptive and non-recursive signal analysis method. The method is distinguished by the high accuracy of signal separation and noise robustness. In turn, Total Variation Denoising, which is defined in terms of a convex optimization problem, is a widely used regularizer in sparse signal denoising. In the paper, we proposed an approach in which Total Variation Denoising is applied to improve the Variational Mode Decomposition-based denoising method. The proposed method is an alternative to methods that have been already proposed, i.e., the methods combine hard and soft thresholding with Variational Mode Decomposition.
In our research, we found that the proposed approach based on Variational Mode Decomposition and Total Variation Denoising has the ability to improve the accuracy of the reference method. The approach was tested for two different synthetic signals. The used in our studies synthetic signals were corrupted by noise with different short and long dependencies.
The presented in work results show that the proposed novel approach gives a great improvement in signal enhancement and it is a promising direction of future research.
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Brzostowski, K., Świątek, J. (2020). Improving Variational Mode Decomposition-Based Signal Enhancement with the Use of Total Variation Denoising. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_56
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