Moving-window Varying Size 3d Transform-based Video Denoising

In this paper we consider the problem of suppressing additive noise in video data. We propose a transformbased video denoising method in sliding, local 3D variable-sized windows. For every spatial position in each frame we use a block-matching algorithm to collect highly correlated blocks from neighboring frames and form 3D arrays for all predefined window sizes by stacking the matched blocks. An optimal window size is then selected according to the ICI rule and a 3D unitary transform is applied to the selected 3D array. Hard-thresholding on its coefficients attenuates the noise and an inverse 3D transform reconstructs a local estimate of the noise-free signal in the array. The final estimate is a weighted average of the overlapping local ones. Our experiments show that the proposed algorithm outperforms all, known to the authors, video denoising methods, both in terms of objective criteria (L distance) and visual quality.