Neutron imaging (NI) has been widely employed in non-destructive investigations. Since the image quality assessment (IQA) method can be beneficial in reflecting the performance of imaging systems and image processing algorithms, we propose a proof-of-concept IQA method for the NI system based on a deep bilinear convolutional neural network (CNN) framework with two designed datasets. Due to the lack of neutron IQA database, different levels of authentic distortion induced by NI are first simulated on the natural and neutron radiographic images to generate the pre-training and fine-tuning datasets, respectively. Then, the gradient magnitude similarity deviation (GMSD) algorithm and transfer learning method are respectively employed to label the above datasets and optimize the prediction performance. Experimental results demonstrate that the proposed method can maintain good consistency with human perception in predicting the quality scores of both the authentic and enhanced neutron radiographic images.

中子成像（NI）已被广泛用于非破坏性研究中。由于图像质量评估（IQA）方法在反映成像系统和图像处理的性能方面可能会有所帮助在算法上，我们提出了基于深度双线性卷积神经网络（CNN）框架和两个设计数据集的NI系统概念验证IQA方法。由于缺乏中子IQA数据库，因此首先在自然和中子射线照相图像上模拟由NI引起的不同程度的真实失真，以分别生成预训练和微调数据集。然后，分别采用梯度幅度相似度偏差（GMSD）算法和传递学习方法对上述数据集进行标注，优化预测性能。实验结果表明，该方法在预测真实和增强中子射线照相图像的质量得分时，都可以保持与人类感知的良好一致性。