Blur and noise originating from the physical imaging processes degrade the microscope data. Accurate deblurring techniques require, however, an accurate estimation of the underlying point-spread function (PSF). A good representation of PSFs can be achieved by Zernike Polynomials since they offer a compact representation where low-order coefficients represent typical aberrations of optical wavefronts while noise is represented in higher order coefficients. A quantitative description of the noise distribution (Gaussian) over the Zernike moments of various orders is given which is the basis for the new soft clipping approach for denoising of PSFs. Instead of discarding moments beyond a certain order, those Zernike moments that are more sensitive to noise are dampened according to the measured distribution and the present noise model. Further, a new scheme to combine experimental and theoretical PSFs in Zernike space is presented. According to our experimental reconstructions, using the new improved PSF the correlation between reconstructed and original volume is raised by 15% on average cases and up to 85% in the case of thin fibre structures, compared to reconstructions where a non improved PSF was used. Finally, we demonstrate the advantages of our approach on 3D images of confocal microscopes by generating visually improved volumes. Additionally, we are presenting a method to render the reconstructed results using a new volume rendering method that is almost artifact-free. The new approach is based on a Shear-Warp technique, wavelet data encoding techniques and a recent approach to approximate the gray value distribution by a Super spline model.