An Improved Motion-Compensated 3-D LLMMSE Filter With Spatio–Temporal Adaptive Filtering Support
In this paper, we propose a motion-compensated spatio-temporal Locally adaptive linear minimum mean squared-error (LLMMSE) filter for noisy image sequences with both temporal and spatially adaptive filtering support. Motion compensation and an adaptive temporal filtering support (TFS) structure guarantee the uniformity of the TFS. An intelligent pixel aggregation algorithm is proposed to include homogeneous neighboring pixels and exclude the outlier pixels, resulting in uniform spatial filtering support. By using the proposed spatio-temporal LLMMSE filter with uniform spatio-temporal support, we can reduce noise efficiently without introducing visually disturbing blurring artifacts. Furthermore, we employ an adaptive weighted local mean and variance estimation algorithm to improve the accuracy of estimation. The weights provide an implicit mechanism for deemphasizing the contribution of the outlier pixels which are wrongly kept within the support to avoid blurring. The performance of the proposed filter is quantitatively evaluated and compared with state-of-the-art methods. The results demonstrate that the proposed filter can achieve superior filtering performance, both subjectively and objectively.