Block-Adaptive Renyi Entropy-Based Denoising for Non-Stationary Signals

This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal's useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and local Renyi entropy estimation which are limited over short time blocks of the signal spectrogram. Local estimation of the signal features reduces the denoising problem to the stationary noise case. Results, presented for synthetic and real data, show consistently better performance gained by the proposed adaptive method compared to denoising driven by global criteria.

Automated summary

This paper proposes a denoising method based on amplitude segmentation and local Renyi entropy estimation criteria limited over short time blocks, reducing the denoising problem to the stationary noise case. Results demonstrate consistently better performance compared to denoising driven by global criteria for both synthetic and real data.