GAN-Based Low-Dose Chest X-Ray Super-Resolution with Hybrid Channel-Spatial Attention and Pooling Layer Removal

Chest X-ray (CXR) imaging is one of the most widely used techniques for screening and diagnosing pulmonary diseases. However, discerning subtle structural changes, such as small nodules, disordered pulmonary textures, tiny cavities, pleural thickening, or spiculation, is difficult using low-resolution images. Acquiring high-resolution CXRs typically requires higher radiation doses, posing a risk to patients. We propose a chest X-ray image super-resolution algorithm based on generative adversarial networks (GAN). Through adversarial training, our approach generates high-resolution CXRs with enhanced details and improved realism. We further incorporate a CSA hybrid attention module into the network, strengthening its ability to capture fine structures and improve texture fidelity. Moreover, we remove the pooling layer from the channel attention module to overcome limitations in super-resolution, thereby preserving spatial information more effectively. Experiments demonstrate our method’s superior performance and robustness, achieving a PSNR of 37.91 and SSIM of 0.9108 on the internal test set while consistently outperforming other methods on previously unseen external clinical datasets. After adversarial training, the method attains optimal visual performance, with LPIPS reduced to 0.0915, and the visual effect improved by 36.4% compared to low-resolution images. Ablation studies further verify the contribution of the proposed method to enhancing super-resolution capability. Overall, results indicate that the proposed method can obtain high-quality chest X-rays images from simulated low-quality inputs.