Compara mètodes

	Signal Denoising	Descomposició Variacional de Modes (VMD)
Camp	Processament de senyals	Processament de senyals
Família	Machine learning	Machine learning
Any d'origen	1995	2014
Autor original	David Donoho	Konstantin Dragomiretskiy & Dominique Zosso
Tipus	Non-parametric signal estimation	Adaptive variational signal decomposition algorithm
Font seminal	Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.
Àlies	Wavelet Shrinkage, Donoho-Johnstone Denoising, Soft Thresholding Denoising, Sinyal Gürültü Giderme	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
Relacionats	3	2
Resum	Wavelet signal denoising, introduced by David Donoho in 1995, is a non-parametric technique that removes noise from one-dimensional or multidimensional signals by decomposing them into wavelet coefficients, suppressing small coefficients that likely represent noise via a soft-thresholding operator, and reconstructing a smooth estimate. It is widely used in biomedical signal processing, geophysics, audio engineering, and image analysis where the underlying signal is assumed to be sparse or piecewise smooth.	Variational Mode Decomposition (VMD) is a fully adaptive, non-recursive signal decomposition method introduced by Konstantin Dragomiretskiy and Dominique Zosso in 2014. It decomposes a real-valued input signal into a discrete number of sub-signals, called intrinsic mode functions (IMFs), each with a specific sparsity in the frequency domain. Unlike Empirical Mode Decomposition, VMD frames decomposition as a variational optimization problem solved via the Alternating Direction Method of Multipliers (ADMM), yielding robust and physically meaningful components.