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

Konnik, Mikhail; Welsh, James

doi:10.48550/arxiv.1412.4031

Cited by 26 publications

(32 citation statements)

References 60 publications

(101 reference statements)

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“…Concurrently, a flow-based generative model [54] is proposed to formulate the distribution of real noise using latent variables with tractable density, and a GAN-based model is presented to learn camera-aware noise model [55]. However, these approaches oversimplify the modern sensor imaging pipeline, especially the noise sources caused by camera electronics, which have been extensively studied in the electronic imaging community [56], [57], [58], [59], [60], [61], [62], [63], [64], [65].…”

Section: Computation-based Approachmentioning

confidence: 99%

“…Noise present during the electrons-to-voltage stage depends on the circuit design and processing technology used, and thus is referred to as pixel circuit noise [58]. It includes thermal noise, reset noise [56], source follower noise [69] and banding pattern noise [58]. The physical origin of these noise components can be found in the electronic imaging literature [56], [58], [64], [69].…”

Section: From Electrons To Voltagementioning

confidence: 99%

“…It includes thermal noise, reset noise [56], source follower noise [69] and banding pattern noise [58]. The physical origin of these noise components can be found in the electronic imaging literature [56], [58], [64], [69]. For instance, source follower noise is attributed to the action of traps in silicon lattice which randomly capture and emit carriers; banding pattern noise, consisting of row and column artifacts, is associated with the CMOS circuit readout pattern and the amplifier gain mismatch.…”

Section: From Electrons To Voltagementioning

confidence: 99%

“…Read noise is generally assumed to follow a Gaussian distribution, but the analysis of noise data (in Section 3.2) tells a long-tailed nature of its shape. This can be attributed by the flicker and random telegraph signal components of source follower noise [58], or the dark spikes raised by dark current [56]. Therefore, we propose using a statistical distribution that can better characterize the long-tail shape.…”

Section: Read Noisementioning

confidence: 99%

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Physics-based Noise Modeling for Extreme Low-light Photography

Wei¹,

Fu²,

Zheng³

et al. 2021

Preprint

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Enhancing the visibility in extreme low-light environments is a challenging task. Under nearly lightless condition, existing image denoising methods could easily break down due to significantly low SNR. In this paper, we systematically study the noise statistics in the imaging pipeline of CMOS photosensors, and formulate a comprehensive noise model that can accurately characterize the real noise structures. Our novel model considers the noise sources caused by digital camera electronics which are largely overlooked by existing methods yet have significant influence on raw measurement in the dark. It provides a way to decouple the intricate noise structure into different statistical distributions with physical interpretations. Moreover, our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms. In this regard, although promising results have been shown recently with deep convolutional neural networks, the success heavily depends on abundant noisy-clean image pairs for training, which are tremendously difficult to obtain in practice. Generalizing their trained models to images from new devices is also problematic. Extensive experiments on multiple low-light denoising datasets -including a newly collected one in this work covering various devices -show that a deep neural network trained with our proposed noise formation model can reach surprisingly-high accuracy. The results are on par with or sometimes even outperform training with paired real data, opening a new door to real-world extreme low-light photography.

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Section: Computation-based Approachmentioning

confidence: 99%

Section: From Electrons To Voltagementioning

confidence: 99%

Section: From Electrons To Voltagementioning

confidence: 99%

Section: Read Noisementioning

confidence: 99%

See 2 more Smart Citations

Physics-based Noise Modeling for Extreme Low-light Photography

Wei¹,

Fu²,

Zheng³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…This section describes our numerical forward model (denoted as F above) of the imaging process, starting with the scene irradiance and ending with a fully formed raw image. We generally follow the noise estimation of Konnik and Welsh [21], while paying particular attention to timedependent properties and visibly dominant factors at short exposures.…”

Section: Sensor Forward Modelmentioning

confidence: 99%

Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times

Dahary¹,

Jacoby²,

Bronstein³

2020

Preprint

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Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst's exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixedexposure burst.

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Real‐Time Noise Source Estimation of a Camera System from an Image and Metadata

Wischow,

Irmisch,

Boerner

et al. 2024

Advanced Intelligent Systems

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Autonomous machines must self‐maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. In this work, a real‐time, memory‐efficient, and reliable noise source estimator that combines data‐based and physically based models is investigated. To this end, a deep neural network that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real‐world noise from two camera systems, and real‐field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug‐and‐play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback loop to approach fully reliable machines.

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

Cited by 26 publications

References 60 publications

Physics-based Noise Modeling for Extreme Low-light Photography

Physics-based Noise Modeling for Extreme Low-light Photography

Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times

Real‐Time Noise Source Estimation of a Camera System from an Image and Metadata

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