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Abstract This article proposes an efficient wavelet-based depth video denoising approach based on a multihypothesis motion estimation aimed specifically at time-of-flight depth cameras. We first propose a novel bidirectional block matching search strategy, which uses information from the luminance as well as from the depth video sequence. Next, we present a new denoising technique based on weighted averaging and wavelet thresholding. Here we take into account the reliability of the estimated motion and the spatial variability of the noise standard deviation in both imaging modalities. The results demonstrate significantly improved performance over recently proposed depth sequence denoising methods and over state-of-the-art general video denoising methods applied to depth video sequences.

Denoising algorithm for the 3D depth map sequences based on multihypothesis motion estimation