mlsanityData to 7 Oct 2026

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

arXiv 1412.4031 ↗First posted 11 Dec 2014astro-ph.IM cs.CVcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2014.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 4; 2024: 5; 2025: 3; 2026: 1.

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  1. 2021Physics-based Noise Modeling for Extreme Low-light Photographycited by 67
  2. 2021Rethinking Noise Synthesis and Modeling in Raw Denoisingcited by 62
  3. 2022Dancing under the stars: video denoising in starlightcited by 47
  4. 2022Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modelingcited by 23
  5. 2023Towards General Low-Light Raw Noise Synthesis and Modelingcited by 19
  6. 2022Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environmentscited by 18
  7. 2021Progressive Joint Low-light Enhancement and Noise Removal for Raw Imagescited by 16
  8. 2024DualDn: Dual-domain Denoising via Differentiable ISPcited by 11
  9. 2023Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoisingcited by 10
  10. 2022INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditionscited by 8
  11. 2021Photonic Differential Privacy with Direct Feedback Alignmentcited by 5
  12. 2023Robust estimation of exposure ratios in multi-exposure image stackscited by 3
11 more
  1. 2021Monitoring and Adapting the Physical State of a Camera for Autonomous Vehiclescited by 2
  2. 2023Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Modelcited by 2
  3. 2024Autobiasing Event Camerascited by 2
  4. 20252-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisitioncited by 2
  5. 2021Snapshot HDR Video Construction Using Coded Maskcited by 0
  6. 2024Real-time Noise Source Estimation of a Camera System from an Image and Metadatacited by 0
  7. 2024Technique Report of CVPR 2024 PBDL Challengescited by 0
  8. 2024Video Denoising in Fluorescence Guided Surgerycited by 0
  9. 2025A Real-time Endoscopic Image Denoising Systemcited by 0
  10. 2025Physics-Guided Rectified Flow for Low-light RAW Image Enhancementcited by 0
  11. 2026Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditionscited by 0