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BEADS: Baseline Estimation And Denoising w/ Sparsity

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Essentials: BEADS paper : Baseline Estimation And Denoising w/ Sparsity BEADS Matlab toolbox BEADS Baseline toolbox at MatlabCentral BEADS page: references, toolboxes and uses Most signals and images can be split into broad classes of morphological features . There are five traditional classes, with potentially different names, although the dams are not fully waterproof: smooth parts: trends, backgrounds, wanders, continuums, biases, drifts or baselines, ruptures, discontinuities: contours, edges or jumps, harmonic parts: oscillations, resonances , geometric textures, hills: bumps, blobs or peaks, noises: un-modeled, more or less random, unstructured or stochastic. In analytical chemistry,  many types of signals (chromatography, mass spectroscopy, Raman, NMR) resemble Fourier spectra: a quantity of positive bump-like peaks, representing the proportion of chemical compounds or atoms, over an instrumental baseline, with noise. Individual  isolated peaks Supe