Research on deep learning restoration algorithm of X-ray backscatter imaging based on virtual training dataset

Abstract

The X-ray lobster eye lens, an innovative technique for focusing high-energy radiation, enables wide-field X-ray imaging. However, its inherent cross point spread function introduces noise and degradation into the resultant images. Conventional image restoration methods are inadequate for suppressing such noise. This paper introduces a backscatter image restoration technique utilizing a virtual training dataset. By convolving the point spread function (PSF) with an object to simulate the image degradation process, the method generates a multitude of convolved images for deep learning training, eliminating the need for manual annotation. Given the high structural similarity between the synthetic convolved images and actual backscatter images, the trained model effectively restores real backscatter images. The restoration process yields a structural similarity index (SSIM) of 0.86 and a mean intersection over union (MIoU) of 0.83 when compared to the reference images. This approach mitigates the limitations of sparse real backscatter datasets, substantially reducing image acquisition time, decreasing radiation flux, and enhancing system safety.