Wavelet-based 3D Data Cube Denoising Using Three Scales of Dependency

In this paper, we propose a novel method for 3D data cube denoising, where the 3D data cube is corrupted by noise with spatially varying noise levels. We perform 3D dual tree complex wavelet transform (DTCWT) to the 3D data cube, and then conduct wavelet-based thresholding based on three scales of dependency in wavelet coefficients. Instead of using the global noise level, we estimate the noise levels locally, which improve the denoising results substantially. We conduct inverse DTCWT to obtain the noise reduced data cubes. Experiments demonstrate that our proposed method outperforms block matching and 3D filtering, video block matching and 3D filtering, 2D bivariate shrinkage, and 3D bivariate shrinkage significantly for noise reduction of 3D data cubes.