To remove the Poisson noise from the X-ray images, in this paper, it was proposed that noise was reduced by using Nonlinear Principal Component Analysis (NLPCA) from the X-ray image sequence. At first, an X-ray image sequence was sampled and the Poisson noise in images was converted into Gaussian noise through Anscombe transform; every noisy image was regarded as a combination of the noise components and the signal component, and then NLPCA was used to separate the signal component from the noise components to reduce noise; the final denoised image was obtained by using Anscombe inverse transform. The results show that, when the number of noisy images in the sequence increases from 2 to 50, the proposed denoising method increases the noisy Shepp-Logan image′s PSNR value from 28.289 4 dB to 37.267 8 dB and increases the SSIM value from 0.700 7 to 0.963 8. Compared with other denoising methods, the proposed denoising method can preserve more image details while reducing the Poisson noise.