Noise modeling and estimation in image sequences from thermal infrared cameras

In this paper we present an automated procedure devised to measure noise variance and correlation from a sequence, either temporal or spectral, of digitized images acquired by an incoherent imaging detector. The fundamental assumption is that the noise is signal-independent and stationary in each frame, but may be non-stationary across the sequence of frames. The idea is to detect areas within bivariate scatterplots of local statistics, corresponding to statistically homogeneous pixels. After that, the noise PDF, modeled as a parametric generalized Gaussian function, is estimated from homogeneous pixels. Results obtained applying the noise model to images taken by an IR camera operated in different environmental conditions are presented and discussed. They demonstrate that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and spatially autocorrelated. Temporal correlation has been investigated as well and found to depend on the frame rate and, by a small extent, on the wavelength of the thermal radiation.