High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial

TL;DR

This paper reviews and develops a comprehensive high-level numerical model for simulating realistic noise in CCD and CMOS photosensors, aiding the testing of image processing algorithms with synthetic noisy images.

Contribution

It introduces a detailed high-level noise model for CCD and CMOS sensors, including various noise sources and non-linearities, validated through experiments and comparison with real sensor data.

Findings

The model accurately replicates noise characteristics of real sensors.

Simulated images match experimental data, validating the model.

Provides a tool for testing image processing algorithms under realistic noise conditions.

Abstract

In many applications, such as development and testing of image processing algorithms, it is often necessary to simulate images containing realistic noise from solid-state photosensors. A high-level model of CCD and CMOS photosensors based on a literature review is formulated in this paper. The model includes photo-response non-uniformity, photon shot noise, dark current Fixed Pattern Noise, dark current shot noise, offset Fixed Pattern Noise, source follower noise, sense node reset noise, and quantisation noise. The model also includes voltage-to-voltage, voltage-to-electrons, and analogue-to-digital converter non-linearities. The formulated model can be used to create synthetic images for testing and validation of image processing algorithms in the presence of realistic images noise. An example of the simulated CMOS photosensor and a comparison with a custom-made CMOS hardware sensor is presented. Procedures for characterisation from both light and dark noises are described. Experimental results that confirm the validity of the numerical model are provided. The paper addresses the issue of the lack of comprehensive high-level photosensor models that enable engineers to simulate realistic effects of noise on the images obtained from solid-state photosensors.