Сравнение методов

	Вейвлет-фильтрация сигналов (мягкое пороговое преобразование)	Преобразование Фурье и спектральный анализ (БПФ)
Область	Обработка сигналов	Обработка сигналов
Семейство	Machine learning	Machine learning
Год появления	1995	1965
Автор метода	David Donoho	James Cooley & John Tukey (FFT)
Тип	Non-parametric signal estimation	Frequency-domain decomposition algorithm
Основополагающий источник	Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.	Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301.
Другие названия	Wavelet Shrinkage, Donoho-Johnstone Denoising, Soft Thresholding Denoising, Sinyal Gürültü Giderme	Fast Fourier Transform, Discrete Fourier Transform, Spectral Analysis, Fourier Dönüşümü
Связанные	3	2
Сводка	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.	The Fourier Transform decomposes a time-domain signal into its constituent sinusoidal frequencies, revealing the spectral content hidden within complex waveforms. Joseph Fourier introduced the continuous transform in 1822, but the computationally efficient Fast Fourier Transform (FFT) was formalized by James Cooley and John Tukey in 1965. Their landmark algorithm reduced the computational complexity from O(N²) to O(N log N), making large-scale spectral analysis practical across engineering, physics, and data science.