Pulsed radiation image restoration based on unsupervised deep learning

Abstract

The process of pulsed X-ray imaging generates noise and blurring that significantly degrade image quality, lead to loss of image information, and interfere with accurate diagnosis of X-ray spot size and objects. Traditional radiation image restoration methods lack flexibility and generalization. Furthermore, neural network methods based on supervised learning require many actual image data sets to be obtained in advance, which limits network training. In this paper, we propose an unsupervised radiation image restoration method based on a deep image prior. The classical DeepRED framework is extended by adding weight-adaptive variational regularization. Automatic strategies are employed to estimate local regularization parameters, resulting in better noise removal and preservation of image edges and texture structures. The proposed method is evaluated through numerical simulations and experimental studies of X-ray source images and flash radiographic images. The results indicate that the proposed method is effective in restoring pulsed radiation images, removing noise, and preserving boundary region information. Additionally, this method produces satisfactory results for the restoration of real neutron images, demonstrating its applicability in the field of radiation imaging.