The neutron imaging system is constrained by physical limitations, including blurring, down-sampling, and noise, which lead to reduced spatial resolution and increased exposure time. This paper proposes a novel single image super-resolution (SISR) algorithm for neutron imaging systems using a diffusion model. A pre-trained diffusion model is employed to learn the prior distribution of natural images. The imaging model of the neutron imaging system is integrated into the diffusion model as a conditional probability to guide the diffusion process. This approach preserves data consistency based on the imaging model while leveraging the generative capabilities of the diffusion model. The results demonstrate that the proposed algorithm effectively suppresses noise and artifacts while recovering rich image details. Furthermore, the proposed algorithm exhibits strong robustness to noise, potentially reducing the exposure time required by the neutron imaging system. Notably, by applying the SISR technique to increase the pixel count in the reconstructed image, both simulated and experimental results indicate that the proposed algorithm not only significantly improves the optical resolution determined by the imaging system blur but also overcomes the resolution limits imposed by the pixel size of the recorded image, achieving sub-pixel resolution.

Neutron imaging system,Single image super-resolution,Diffusion model,Sub-pixel resolution