Enhanced X-ray image denoising via the synergy of linear attention and convolution

X-ray imaging technology, as the core non-invasive inspection method, plays an irreplaceable role in industrial non-destructive testing and medical diagnosis. However, during signal acquisition, the imaging system faces multiple interferences, such as the quantum effect and electronic noise. This leads to a significant decrease in the image's signal-to-noise ratio, seriously affecting the accuracy of hazardous material identification and lesion detection. Existing X-ray image denoising methods have two major limitations. First, in physical model-driven denoising methods, the existing noise models deviate significantly from realistic ones, resulting in poor denoising results. Second, in mainstream deep learning-based methods, Convolutional Neural Networks (CNNs) have limitations in capturing long-range dependencies, while the Transformer model with a global receptive field has high computational complexity. To address these challenges, a physically grounded noise model is designed for synthesizing realistic X-ray images, trained on the public mainstream X-ray image security inspection datasets and augmented with hybrid real-synthetic data. Based on this, a novel denoising model, XDenoiser, is proposed in this paper. It incorporates a linear attention complexity Receptance Weighted Key-Value (RWKV) into a Transformer-based image restoration structure and combines it with CNNs to support both global and local receptive fields. Experiments on the expanded mainstream X-ray image security inspection datasets demonstrate the reasonableness and effectiveness of the XDenoiser algorithm.