Modelling dark current and hot pixels in imaging sensors

TL;DR

This paper develops a complex Gaussian mixture model to analyze dark current and hot pixels in digital sensors, revealing multiple pixel types and complex growth patterns with exposure and temperature.

Contribution

It introduces a novel latent class Gaussian mixture model that captures the complex variance and pixel behavior related to dark current and hot pixels in imaging sensors.

Findings

Identifies at least two types of hot pixels with distinct behaviors.

Shows dark current growth cannot be modeled simply by parametric functions.

Sensor non-uniformity increases with exposure time and temperature.

Abstract

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