Volume Data Denoising via Extended Weighted Least Squares

During the data acquisition procedure, volume data are usually contaminated by noises. This would create visual confusion and misunderstanding in analyzing the volume data. Thus, noise reduction is necessary for improving the quality of volume data inspection and analytic tasks. However, it is far from being fully resolved in removing noise while maximally retaining geometric sharp features. In this paper, we present a powerful volume denoising method based on the extended weighted least squares. We improve the weighted least squares method and extend it to 3D for volume data denoising. The primary advantage of the proposed method is that it can consistently produce better results in removing noise while preserving sharp features. We illustrate our technique on synthetic and real-world 3D data and compare our method with the median method, weighted least squares, L0 volume gradient minimization, and edge aware anisotropic diffusion method. The experimental results demonstrate that our method can achieve higher quality results than the selected state-of-the-art methods.