X-ray CT, which is widely used as a non-destructive inspection method, has the problem of long measurement time. If the measurement time is too short, the transmitted image will be blurred or noisy, and the quality of CT volume will be reduced. Therefore, there is a trade-off between time reduction and quality. In this research, we aim to develop a method that can both shorten time and improve the quality. We train CNNs to improve image quality on a previously obtained dataset, and then apply the CNNs to another dataset. With the loss function proposed in this study, we can achieve high quality output results of CNN and we evaluated it with quantitative metrics and visuals.
