关键词: 优化-最小值算法, 全变分, 差值, 叠加迭代

Abstract:

With respect to denoising of the sparse signal with large peak-to-peak transitions, this paper puts forward a denoising algorithm of superposition iteration after total variation. Such denoising algorithm can not only effectively mitigate noise of the signal in the large transition zone, but also retain its original frame. Specifically, the signal after continuous total variation-based denoising is differenced, and the resultant difference is superimposed based on the original signal, so that it not only covers the information of the original signal but also retains the previous denoising information. Furthermore, noise of the sparse signal with large peak-to-peak transitions will be effectively mitigated through iterative denoising. Compared with the optimization-minimum algorithm, the experimental results are closer to the original signal.

Key words: majorization-minimum algorithm, total variation denoising, differenced, superposition iteration