PENDANTSS: Noise, Trend and Sparse Spikes separation

PENDANTSS performs denoising, detrending and deconvolution for sparse peak-like signals (e.g. from analytical chemistry: chromatography)


Updated 6 Feb 2023

View License

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peaklike signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by lowpass filtering. We combine the generalized pseudo-norm ratio SOOT/SPOQ sparse penalties l_p/l_q with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals.

Cite As

Paul Zheng, Emilie Chouzenoux, Laurent Duval (2023). PENDANTSS: Noise, Trend and Sparse Spikes separation (, MATLAB Central File Exchange. Retrieved February 6, 2023.

Paul Zheng, Emilie Chouzenoux, Laurent Duval. PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes. Preprint, 2023.

MATLAB Release Compatibility
Created with R2022b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes

Updated references