Fast AWGN reduction in videos using change detection
In this paper, two low-complexity filters, which are based on a novel filter structure consisting of both the spatial and temporal estimation processes and use a change detection technique to measure the interframe motion, are proposed to reduce additive white Gaussian noise (AWGN) in videos. Both the filters use the edge-adaptive Wiener filter for spatial estimation, while for temporal estimation two schemes are proposed, one based on the scalar Kalman filter and the other on the running average filter. These temporal estimation techniques are carded out on the spatial estimate to get the spatiotemporal estimate. The final estimate is obtained by a suitable adaptive combination of the spatial and the spatiotemporal estimates. The use of a change detection technique to measure the interframe motion results in a computational complexity, which is considerably lower than that of the existing filters using motion estimation and compensation technique. The qualitative and quantitative performance of the proposed filters are studied and compared with that of some of the existing ones. It is found that the proposed filters have much less processing time and also have better noise reduction ability compared to the others.