We develop a new filter which combines spatially adaptive noise filtering in the wavelet domain and temporal filtering in the signal domain. For spatial filtering, we propose a new wavelet shrinkage method, which estimates how probable it is that a wavelet coefficient represents a "signal of interest" given its value, given the locally averaged coefficient magnitude and given the global subband statistics. The temporal filter combines a motion detector and recursive time-averaging. The results show that this combination outperforms single resolution spatio-temporal filters in terms of quantitative performance measures as well as in terms of visual quality. Even though our current implementation of the new filter does not allow real-time processing, we believe that its optimized software implementation could be used for real- or near real-time filtering.