Detecting Nuclear Radiation with an Uncovered CMOS Camera &amp; a Long-Wavelength Pass Filter

For the problems of complex noise and color cast in the images captured by complementary metal oxide semiconductor(CMOS) image sensors in the gamma radiation environment, this paper proposed a joint temporal correlation denoising and color cast correction approach for the gamma radiation scene image clarification. Concretely, we first adaptively stretch the difference between noise and background to improve the salience of weak noise, making noise detection results more accurate. Secondly, the temporal transient characteristics of gamma radiation noise are utilized for noise detection, and these noisy pixels are repaired by exploiting the temporal correlation characteristics of background pixels. Lastly, the Bayer-Guided auto white balance(AWB) method is proposed to solve the problem of dysregulated color in the gamma radiation scene image, and the color level remapping approach is applied to eliminate the problem of insufficient dynamic range. Extensive experiments are carried out on the images captured from the Co60 gamma radiation scene. Compared with the source images, our method improves the peak signal to noise ratio(PSNR) by 6.3(dB) and structural similarity(SSIM) by 0.34. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in improving the clarity of gamma radiation scene images.

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Nuclear radiation detection based on the convolutional neural network under public surveillance scenarios

Yan

Zhang

et al. 2022

Open Physics

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Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method.

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Detecting Nuclear Radiation with an Uncovered CMOS Camera & a Long-Wavelength Pass Filter

Cited by 3 publications

References 5 publications

$$\gamma $$ radiation image denoising method based on speckle splitting

$$\gamma $$ radiation image denoising method based on speckle splitting

A gamma radiation scene image clarification method based on spatiotemporal combination

Nuclear radiation detection based on the convolutional neural network under public surveillance scenarios

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