Abstract: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 th… Show more
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