The utilization of dual-energy X-ray detection technology in security inspection plays a crucial role in ensuring public safety and preventing crimes. However, the X-ray images generated in such security checks often suffer from substantial noise due to the capture process. The noise significantly degrades the quality of the displayed image and affects the performance of the automatic threat detection pipeline. While deep learning-based image denoising methods have shown remarkable progress, most existing approaches rely on large training datasets and clean reference images, which are not readily available in security inspection scenarios. This limitation hampers the widespread application of these methods in the field of security inspection. In this paper, we addressed a denoising problem designed for X-ray images, where the noise model follows a Poisson-Gaussian distribution. Importantly, our method does not require clean reference images for training. Our denoising approach is built upon the Blindspot neural network, which effectively addresses the challenges associated with noise removal. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real X-ray image dataset. The results indicate that our method achieves favorable BRISQUE scores across different baggage scenes.