In the γ radioactive environment, high-energy photons induce degradation of the image sensor, which effects feature detection and tracking in the Visual Inertial Odometry (VIO) algorithm and deteriorates its localization performance. To address this issue, in this work, we propose a monocular VIO method using edge-based point features. To mitigate the effects of radiation noise, firstly, in the image preprocessing module, the median filter is used for real-time image denoising. Secondly, in the data association module, both Shi-Tomasi and edge-based point features are detected. The edge-based point feature is the endpoint or corner point in the salient edge map, which is more robust to radiation noise. Then, the bi-directional motion parallaxes and the RANdom SAmple Consensus (RANSAC) method are exploited to reject outliers. Finally, the point features measurements and Inertial Measurement Unit (IMU) pre-integration measurements are added into a tightly-coupled sliding window optimization VIO framework for localization estimation. The proposed method is verified by synthetic and real γ radioactive environment datasets. The experimental results show that the proposed method achieves more accurate and robust localization than the state-of-the-art VIO approaches in the γ radioactive environments.

For the problems of darkness, insufficient contrast, and color cast in the images captured by CMOS image sensors in the γ radiation environment, this paper proposed a joint contrast improvement and color cast correction approach for the γ radiation image enhancement, which improved the image effects from enhancing detail expression and color expression. For the problem of overall darkness of γ radiation image, we firstly utilized the logarithmic mapping to improve the image brightness. Secondly, for the insufficient contrast of image details, a contrast enhancement method with an adaptive Gamma coefficient is proposed, which adaptively adjusts the image brightness at the pixel level, so that the image detail expression is more in line with the human visual characteristics (HVC). Lastly, the color level remapping method is exploited for color cast correction. Extensive experiments are conducted on the γ radiation images collected in the Co60 environment. With the help of the proposed method, we can achieve the best results in quantitative and visual comparison. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in γ radiation image enhancement.INDEX TERMS γ radiation image, image enhancement, non-linear mapping, contrast improvement, color cast correction.

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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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