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This paper demonstrates wavelet-denoising approach using polynomial threshold operators in 3-dimensional applications. This paper compares the efficacy of different denoising algorithms on 3D biomedical images using 3D wavelet transform. The denoising mechanism is demonstrated by mitigating noise of different variances using polynomial thresholding. Our approach is to apply a parameterized threshold and optimally choose the parameters for high performance noise suppression depending on the nature of the images and noise.Comparative studies in the wavelet domain conclude that the presented method is viable for 3D applications. It also confirms the feasibility in using the polynomial threshold operators as a wavelet-polynomial threshold based interpolation filter. The filter applied to assist three spatial-based interpolation algorithms (i.e. Nearest-neighbor, Bilinear, and Bicubic) and to a spectral wavelet-based interpolation algorithm. Simulation shows that the denoising using polynomial threshold operators mitigates distortions for the interpolation.