PENDANTSS: Noise, Trend and Sparse Spikes separation

版本 1.0.01 (843.8 KB) 作者: Laurent Duval
PENDANTSS performs denoising, detrending and deconvolution for sparse peak-like signals (e.g. from analytical chemistry: chromatography)
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更新时间 2023/2/6

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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.

引用格式

Paul Zheng, Emilie Chouzenoux, Laurent Duval (2023). PENDANTSS: Noise, Trend and Sparse Spikes separation (https://www.mathworks.com/matlabcentral/fileexchange/124425), 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. https://arxiv.org/abs/2301.01514

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