基于神经网络的大孔径厚针孔成像复原算法研究

为了更好地获取低强度辐射源空间分布图像，提出一种使用神经网络算法将大孔径厚针孔退化图像复原的方法。建立了孔径5 mm、10 mm、15 mm的厚针孔模型，获得了3600个汉字形状辐射源的厚针孔退化图像集。基于DnCNN神经网络模型，建立了大孔径厚针孔退化图像复原神经网络，并与维纳滤波、Lucy-Richardson这些传统算法进行了比较。在考虑噪声影响后，利用迁移学习理论，对原神经网络模型进行迁移训练，再对含噪大孔径厚针孔退化图像进行复原。神经网络算法复原的RMSE明显低于传统方法，迁移学习显著减小了噪声的影响。证明了神经网络算法在大孔径厚针孔退化图像复原领域的优越性，并验证了神经网络方法复原含噪大孔径厚针孔退化图像的可行性。

Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network

To obtain the spatial distribution image of low intensity radiation source better, a method is proposed to restore large aperture thick pinhole degraded image using neural network algorithm. The thick pinhole model of 5 mm, 10 mm and 15 mm apertures is established, and the degenerate image sets of thick pinhole for the shape radiation source of 3600 Chinese characters are obtained. Based on the DnCNN neural network model, the neural network for image restoration with large aperture and thick pinhole is obtained, and compared with traditional algorithms such as Wiener filter and Lucy Richardson. After considering the influence of noise, the original neural network model is trained by means of transfer learning theory, and then the degraded image of large aperture pinhole with noise is restored. The RMSE of neural network algorithm is significantly lower than that of the traditional one, and the effect of noise is greatly improved by transfer learning. This paper proves the superiority of neural network algorithm in the field of image restoration with large aperture and thick pinhole, and verifies the feasibility of neural network method to restore the large aperture thick pinhole degraded image with noise.