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. In this correspondence, we describe how the confidence intervals for the 3D noise measurement allows for determination of the sampling necessary to reach a desired precision. We then apply that knowledge to create a smaller cube that can be evaluated spatially across the 2D image giving the noise as a function of position. The method presented here allows for both defective pixel identification and implements the finite sampling correction matrix. In support of the reproducible research effort, the Matlab functions associated with this work can be found on the Mathworks file exchange [1].

Date Published: 3 May 2016
PDF: 10 pages
Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 98200K (3 May 2016); doi: 10.1117/12.2222905

Published in SPIE Proceedings Vol. 9820:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII
Gerald C. Holst; Keith A. Krapels, Editor(s)