Comparer des méthodes

	Acquisition et reconstruction par échantillonnage parcimonieux	Transformée de Fourier à court terme
Domaine	Traitement du signal	Traitement du signal
Famille	Process / pipeline	Process / pipeline
Année d'origine	2006	1946
Auteur d'origine	Emmanuel Candès, Justin Romberg, and Terence Tao	Dennis Gabor
Type	Sparse signal recovery	Time-frequency signal analysis
Source fondatrice	Candes, E. J., Romberg, J., & Tao, T. (2006). Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete and Inaccurate Measurements. IEEE Transactions on Information Theory, 52(2), 489–509.	Gabor, D. (1946). Theory of Communication. Journal of the Institution of Electrical Engineers, 93(3), 429–457.
Alias	Compressed Sensing, CS, Sparse Recovery, Sub-Nyquist Sampling	STFT, Windowed Fourier Transform, Time-Frequency Analysis
Apparentées	4	4
Résumé	Compressive Sensing (CS) is a signal acquisition and reconstruction technique that exploits signal sparsity to recover high-resolution signals from far fewer samples than required by the Nyquist sampling theorem. Developed by Emmanuel Candès, Justin Romberg, and Terence Tao in 2006, compressive sensing challenges the traditional sampling paradigm by showing that signals with sparse representations can be reconstructed from sub-Nyquist random measurements using nonlinear optimization.	The Short-Time Fourier Transform (STFT) is a fundamental signal analysis technique that computes the frequency content of a signal as it evolves over time by applying the Fourier transform to short, overlapping windows of the signal. Introduced conceptually by Dennis Gabor in 1946, the STFT provides a time-frequency representation essential for analyzing non-stationary signals where frequency content changes over time.