What are common noise reduction techniques in signal processing?

Noise reduction approaches: Filtering (lowpass, bandpass for in-band signal, reject out-of-band noise), Averaging (multiple measurements average, reduces random noise by sqrt(N)), Adaptive filtering (LMS, NLMS estimate and subtract correlated noise), Spectral subtraction (estimate noise spectrum, subtract from noisy signal spectrum), Wiener filtering (optimal linear filter minimizing MSE), and Wavelet denoising (threshold small wavelet coefficients, preserve large ones). Choice depends on: Signal and noise characteristics, availability of noise reference, real-time requirements, and acceptable artifacts.