Joint Edge-Guided and Spectral Transformation Network for Self-supervised X-Ray Image Restoration

X-rays are widely utilized in the security inspection field due to their ability to penetrate objects and visualize intricate details and structural features. However, X-ray images often suffer from degradation issues, such as heavy noise and artifacts, which can adversely affect the accuracy of subsequent high-level tasks. Therefore, X-ray image restoration plays a critical role in the applications of X-ray images. Existing supervised restoration methods depend on numerous noisy-clean image pairs for training, which restricts their application to X-ray images. Although there have been a few attempts to train models with single noisy images, they ignored the unique prior knowledge of X-ray images. This result in poor performance with artifacts and inadequate denoising. To tackle these challenges, we propose a novel self-supervised restoration method called the Joint Edge-guided and Spectral Transformation Network (ESTNet), which integrates edge guidance and spectral transformation techniques to restore color X-ray images. Specifically, ESTNet leverages an adaptive edge guidance module to emphasize edge details. In addition, to achieve a balance between noise suppression and detail preservation in image restoration, we propose spatial spectral blocks that enable the network to capture both global and local contextual information. Extensive experiments on real-world images confirm the superiority of ESTNet over state-of-the-art methods in terms of quantitative metrics and visual quality.