Binlets: Data fusion-aware denoising enables accurate and unbiased quantification of multichannel signals

As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. Here, we present a method based on the Haar wavelet transform that trades off resolution against accuracy based on statistical significance. By taking advantage of correlations between channels, it offers a superior performance compared to denoising each channel separately. It outperforms standard wavelet methods when the magnitude of interest in the data-fusion process involves a non-linear transformation or reduction of a multichannel signal. We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two-dimensional image. The method has a simple interpretation as an adaptive binning of the signal, and neither requires training data nor specialized hardware to run fast. In addition, a reference Python implementation is available on GitHub and PyPI, making it simple to integrate into any analysis pipeline.

Automated summary

As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. The method presented here, based on the Haar wavelet transform, offers superior performance by trading off resolution against accuracy and taking advantage of correlations between channels. It outperforms standard wavelet methods in cases involving non-linear transformations or reduction of multichannel signals.