In this article, we propose a post-processing scheme for the novel volumetric microscopy technique SILMAS. We demonstrate this scheme on data from an alpha-synuclein transgenic mouse brain. By combining structured illumination and axial sweeping, a SILMAS measurement provides a prerequisite for quantitative data extraction through improved contrast and optical sectioning. However, due to the technique’s efficient removal ofb multiple scattered light, image artifacts such as illumination inhomogeneity, shadowing stripes, and signal attenuation, are highlighted in the recorded volumes. To suppress these artifacts, we rely on the strengths of the imaging method. The SILMAS data, together with the Beer-Lambert law, allow for an approximation of real light extinction, which can be used to compensate for light attenuation in a near-quantitative way. Shadowing stripes can be suppressed efficiently using a computational strategy thanks to the large numerical aperture of an axially swept light sheet. Here, we build upon prior research that employed wavelet-Fourier filtering by incorporating an extra bandpass step. This allows us to filter high-contrast light sheet microscopy data without introducing new artifacts and with minimal distortion of the data. The combined technique is suitable for imaging cleared tissue samples of up to a centimeter scale with an isotropic resolution of a few microns. The combination of a thin and uniform light sheet, scattered light suppression, light attenuation compensation, and shadowing suppression produces volumetric data that is seamless and highly uniform.