Recursive temporal denoising and motion estimation of video

In this paper, we present a new technique for video denoising which is based on a novel motion estimation algorithm. First, a recursive temporal denoising is performed through the estimated motion trajectory. After that, appropriate spatial filtering is done. The proposed algorithm automatically adapts to the detected noise level, provided it is short-tail noise, such as Gaussian noise. It uses a one-level wavelet decomposition where both motion estimation and denoising is performed. The non-decimated transform is used because it is nearly shift invariant and thus yields better motion estimation and denoising results than the decimated transform. The results on different image sequences demonstrate that the proposed filter outperforms the other state-of-the-art filters both in terms of PSNR and visual aspect.