Noise Reduction in Video Sequences Using Wavelet-Domain and Temporal Filtering

We develop a sequential wavelet domain and temporal filtering scheme, with jointly optimized parameters, which results in high-quality video denoising over a large range of noise levels. In this scheme, spatial filtering is performed by a spatially adaptive Bayesian wavelet shrinkage in a redundant wavelet representation. In the next filtering stage, a motion detector controls selective, recursive averaging of pixel intensities over time. The results demonstrate that the proposed filter outperforms recent single-resolution representatives as well as some recent motion-compensated wavelet based video filters. We also analyze important practical issues for possible industrial applications. In particular, we investigate the performance degradations that result from making the wavelet domain filtering part less complex, by removing the redundancy of the representation and/or by replacing a sophisticated spatially adaptive shrinkage method by soft-thresholding.