Two adaptive approaches for nonstationary filtering of image sequences are presented and experimentally compared. According to the first approach, a recursive spatio-temporal motion- compensated (MC) estimator is applied to the noisy sequence that adapts to the local spatial and temporal signal activity. A separable 3-D estimator is proposed that consists of three coupled 1-D estimators; its input is the noisy image plus additional signals that contain spatial information provided by a simple edge-detector or temporal information provided by the MC backward difference (registration error). The steady-state gain and the parameters of this separable estimator are computed by closed form formulae, thus allowing a very efficient implementation. According to the second approach, the noisy signal is first decomposed into a stationary and a nonstationary part based on an estimate of its local mean and deviation. A minimum variance estimator of the local mean and deviation of the observed signal is used. After the current mean is subtracted from the observed signal and the signal is normalized by using the current deviation, a relatively simple noise filter is used for filtering the stationary part. The above methods are applied to the filtering of noisy video-conference image sequences for various levels of noise. Both methods show a very satisfactory performance taking into consideration their simplicity and computational efficiency.