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	圧縮センシング	短時間フーリエ変換
分野	信号処理	信号処理
系統	Process / pipeline	Process / pipeline
提唱年	2006	1946
提唱者	Emmanuel Candès, Justin Romberg, and Terence Tao	Dennis Gabor
種類	Sparse signal recovery	Time-frequency signal analysis
原典	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.
別名	Compressed Sensing, CS, Sparse Recovery, Sub-Nyquist Sampling	STFT, Windowed Fourier Transform, Time-Frequency Analysis
関連	4	4
概要	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.