Abstract
Total variation signal denoising is an effective nonlinear filtering method, which is suitable for the restoration of piecewise constant signals disturbed by white Gaussian noises. Towards efficient denoising of piecewise constant signals, a new total variation denoising algorithm based on an improved non-convex penalty function is proposed in this paper. While ensuring the objective function is convex, a new non-convex penalty is designed. The denoising efficiency is improved by updating the dynamic total variational adjustment in the penalty function to the static adjustment. Experimental results demonstrated that the proposed denoising algorithm improved the denoising efficiency without affecting the denoising effect. The overall performance was shown better than the current excellent denoising algorithm.
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Acknowledgements
This work was supported by the Hubei Province Natural Science Foundation under Grant 2020CFA031, the Inner Mongolia Natural Science Foundation under Grant 2019BS06004, and the National Natural Science Foundation of China under Grants 61773354 and 61903345.
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Lv, D., Cao, W., Hu, W., Wu, M. (2021). A New Total Variation Denoising Algorithm for Piecewise Constant Signals Based on Non-convex Penalty. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_45
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DOI: https://doi.org/10.1007/978-981-16-5188-5_45
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