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A Gaussian mixture model with a complex covariance structure was used toanalyse experimental data from images recorded by a digital sensor underdarkness, to model the effects of temperature and duration of exposure onartificial signals (dark current), on ordinary and possibly defective (hot)pixels. The model accounts for two components of variance within each latenttype: random noise in each image and lack of uniformity within the sensor; bothcomponents are allowed to depend on experimental conditions. The results seemto indicate that the way dark current grows with the duration of exposure andtemperature cannot be represented by a simple parametric model. The latentclass model detects the presence of at least two types of hot pixels, where theless frequent ones have also a more extreme behaviour. Though the lack ofuniformity of the sensor is amplified by duration of exposure and temperature,pixels characteristics seem to deviate in the same direction and with the samerelative size.

Modelling dark current and hot pixels in imaging sensors