混合二阶全变分的抗核辐射图像降噪方法

为了更好地保留核环境下图像降噪后的细节信息，提出了基于混合二阶全变分的抗核辐射图像降噪方法。将非凸二阶全变分与重叠组稀疏正则化相结合，使用交替方向乘子法(alternating direction method of multiplier, ADMM)和增广拉格朗日乘子法对全局问题进行优化求解，多次迭代后得到基本降噪图像;将多次降噪后的基本降噪图像进行差值迭代，使核辐射图像中大范围跳变的灰度值更加接近原始图像灰度值;根据核噪声的特点，设计算法模拟出核噪声斑块。通过在真实核环境下采集的数据集和模拟的核噪声数据集上进行实验，峰值信噪比(peak signal-to-noise ratio, PSNR)和结构相似性(structural similarity, SSIM)等指标的变化及处理后的视觉效果表明，提出的算法在保留图像细节信息方面优于对比算法。

A hybrid second-order total variational noise reduction method for radiation-resistant images

To better preserve the detailed information of nuclear environment image after noise reduction, we propose a noise reduction method for anti-nuclear radiation image based on hybrid second-order total variation. The method combines non-convex second-order total variation with overlapping group sparse regularization, where non-convex second-order total variation denoises the image, and overlapping group sparse regularization is used to remove artifacts caused by total variation. alternating direction method of multiplier (ADMM) and the augmented Lagrange multiplier method are used to optimize and solve the global problem, and the basic denoised image is obtained after several iterations. Finally, the basic denoised image after multiple denoising is subjected to difference iteration, so that the gray value of the large-scale jump in the nuclear radiation image is closer to the gray value of the original image. Before the experimental verification, according to the characteristics of nuclear noise, an algorithm is designed to simulate the nuclear noise patches. Through experiments on datasets collected in a real nuclear environment and simulated nuclear noise datasets, indicators such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and the visual effect after processing shows that the algorithm is better than the contrast algorithm in preserving the details of the image.