Abstract: Due to the lack of high-quality paired datasets, the application and development of deep learning in neutron computed tomography (CT) reconstruction are severely hindered. Although the imaging principles of neutron CT and photon CT are both based on the Radon transform, the imaging characteristics of the two processes during particle transport are different, so the network trained for photon CT cannot be directly used to solve the reconstruction problem of neutron CT. Therefore, in this work, an unsupervised domain adaptive network is proposed that can solve the probability distribution difference problem in the migration process from photon tomography to neutron tomography. In the proposed method, the maximum mean difference is introduced to reduce the distribution difference between photon and neutron tomography image features, and furthermore, wavelet transform and convolution neural network are combined to enhance the effective features of reconstruction. The comparison experiments with other algorithms show that the proposed method can reconstruct highquality neutron tomography images from low-flux neutron tomography results, effectively alleviating the degradation of low-flux neutron tomography.

Key words: image processing, neutron computer tomography reconstruction, domain adaptive transfer learning, sparse tomography