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

arXiv: https://arxiv.org/abs/1412.4031 · HTML: https://mlsanity.com/paper/1412.4031/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2014.

## Built on by (most cited first, 23 of 23)

- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [Rethinking Noise Synthesis and Modeling in Raw Denoising](https://mlsanity.com/paper/2110.04756/) (2021)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling](https://mlsanity.com/paper/2207.06103/) (2022)
- [Towards General Low-Light Raw Noise Synthesis and Modeling](https://mlsanity.com/paper/2307.16508/) (2023)
- [Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments](https://mlsanity.com/paper/2210.16046/) (2022)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [DualDn: Dual-domain Denoising via Differentiable ISP](https://mlsanity.com/paper/2409.18783/) (2024)
- [Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising](https://mlsanity.com/paper/2310.09126/) (2023)
- [INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions](https://mlsanity.com/paper/2207.08201/) (2022)
- [Photonic Differential Privacy with Direct Feedback Alignment](https://mlsanity.com/paper/2106.03645/) (2021)
- [Robust estimation of exposure ratios in multi-exposure image stacks](https://mlsanity.com/paper/2308.02968/) (2023)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles](https://mlsanity.com/paper/2112.05456/) (2021)
- [Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model](https://mlsanity.com/paper/2308.03448/) (2023)
- [2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition](https://mlsanity.com/paper/2512.03245/) (2025)
- [Snapshot HDR Video Construction Using Coded Mask](https://mlsanity.com/paper/2112.02522/) (2021)
- [Real-time Noise Source Estimation of a Camera System from an Image and Metadata](https://mlsanity.com/paper/2404.03251/) (2024)
- [Technique Report of CVPR 2024 PBDL Challenges](https://mlsanity.com/paper/2406.10744/) (2024)
- [Video Denoising in Fluorescence Guided Surgery](https://mlsanity.com/paper/2411.09798/) (2024)
- [A Real-time Endoscopic Image Denoising System](https://mlsanity.com/paper/2506.15395/) (2025)
- [Physics-Guided Rectified Flow for Low-light RAW Image Enhancement](https://mlsanity.com/paper/2509.08330/) (2025)
- [Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions](https://mlsanity.com/paper/2605.24590/) (2026)

## Abstract (arXiv)

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.