Neutron-image super-resolution based on convolutional neural networks and novel parallel transformer

Abstract

Enhancing the resolution of neutron imaging can be achieved by upgrading the hardware components of the neutron imaging system, including employing more potent neutron source systems, utilizing high-resolution detectors, and implementing superior collimation techniques. However, such improvements are frequently constrained by challenges such as high cost and limited flexibility. In this paper, a flexible and efficient single-neutron-image super-resolution network is proposed for neutron image super-resolution. We propose a novel multi-scale feature extraction network, which can extract features deeply by fusing features of different types and levels. In order to learn more context information, a novel parallel Transformer is proposed to overcome the long dependency problem of sequences. A high-frequency information enhancement network is proposed to enhance the high-frequency information of neutron image in order to address the blur problem. Quantitative and qualitative analyses of benchmark datasets and real neutron images show that the proposed network performs better on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than bicubic interpolation and other methods, and reconstructs high-resolution neutron images that are more consistent with human visual perception, highlighting its application potential in neutron images quality