When evaluated with a spatially uniform irradiance, an imaging sensor exhibits both spatial and temporal variations, which can be described as a three-dimensional (3D) random process considered as noise. In the 1990s, NVESD engineers developed an approximation to the 3D power spectral density (PSD) for noise in imaging systems known as 3D noise. This correspondence describes the decomposition of the full 3D PSD into the familiar components from the 3D Noise model. The standard 3D noise method assumes spectrally (spatio-temporal) white random processes, which is demonstrated to be atypically in the case with complex modern imaging sensors. Using the spectral shape allows for more appropriate analysis of the impact of the noise of the sensor. The processing routines developed for this work consider finite memory constraints and utilize Welch's method for unbiased PSD estimation. In support of the reproducible research effort, the Matlab functions associated with this work can be found on the Mathworks file exchange .