Comparison of the dark signal degradation induced by Gamma ray, proton, and neutron radiation in pinned photodiode CMOS image sensors

Wang, Zujun; Xue, Yexiang; Chen, Wei; Xu, Rui; Ning, Hao; He, Bin; Yao, Zhibin; Liu, Minbo; Sheng, Jiangkun; Ma, Wuying; Dong, Guangyu

doi:10.1007/s11432-018-9554-4

Cited by 4 publications

(1 citation statement)

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“…In this process, the secondary electrons are small in extent and low in energy, but they are also a cause of noise generation. When the secondary electrons replace the original electrons in the image element, this will cause transient pulse currents that will macroscopically manifest as bright spots on the image [ 3 ]. If the radiation causes lattice defects or displacement damage to the sensor, resulting in degradation of its function, the corresponding image element will work abnormally and produce temporary or permanent abnormal output in the subsequent image [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Gamma Radiation Image Noise Prediction Method Based on Statistical Analysis and Random Walk

Deng

Yao

et al. 2022

Sensors

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The gamma radiation environment is one of the harshest operating environments for image acquisition systems, and the captured images are heavily noisy. In this paper, we improve the multi-frame difference method for the characteristics of noise and add an edge detection algorithm to segment the noise region and extract the noise quantization information. A Gaussian mixture model of the gamma radiation noise is then established by performing a specific statistical analysis of the amplitude and quantity information of the noise. The established model is combined with the random walk algorithm to generate noise and achieve the prediction of image noise under different accumulated doses. Evaluated by objective similarity matching, there is no significant difference between the predicted image noise and the actual noise in subjective perception. The ratio of similarity-matched images in the sample from the predicted noise to the actual noise reaches 0.908. To further illustrate the spillover effect of this research, in the discussion session, we used the predicted image noise as the training set input to a deep residual network for denoising. The network model was able to achieve a good denoising effect. The results show that the prediction method proposed in this paper can accomplish the prediction of gamma radiation image noise, which is beneficial to the elimination of image noise in this environment.

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Section: Introductionmentioning

confidence: 99%