Image-based defogging technology can significantly enhance intraoperative image quality and shows great promise in various medical fields. A new image removal algorithm based on conditional generative adversarial networks (cGAN) has been developed. This algorithm employs the Tiramisu model instead of the conventional U-Net, thereby improving its computational accuracy. Additionally, the quality of the resulting images is enhanced by incorporating more textual data. A novel visual perception method is proposed, utilizing a contrast-based approach to improve the similarity between images with the same semantic content. Experiments demonstrate that this method not only excels at fog removal but also better preserves the key visual features of the images. Compared to existing image defogging technologies, this method offers superior qualitative analysis capabilities. This advancement can aid doctors in better visualizing intraoperative images. The effectiveness and robustness of the proposed method are validated through comparative analysis with several existing image noise reduction techniques.
