High quality medical images are the foundation of clinical diagnosis and treatment, but their quality may decrease due to imaging noise, artifacts, and uneven lighting. To address this issue, this paper proposes a novel Multi-scale Deep Residual Shrinkage Generative Adversarial Network (MDRSGAN) for non paired medical image enhancement. Its core innovations include: (1) Adopting a customized generator with learnable channel shared soft threshold (DRSN-CS), which can achieve hierarchical feature extraction and adaptive noise suppression; (2) Combining dual core discriminator to ensure global statistical consistency and high fidelity of local structure; (3) Introducing content perception loss and lighting loss to optimize overall details and image features. The performance of MDRSGAN is validated on fundus retina, endoscope, and self built NIR-II mouse dataset, and its performance is superior to five mainstream methods, such as a 5% increase in SNR for NIR-II mouse image enhancement. Downstream retinal vessel segmentation experiments show that IoU and DSC achieved 13% and 8% improvement, respectively, demonstrating the clinical applicability and performance advantages of this method.
Jiang et al. (Thu,) studied this question.