Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images
Chong Zhang, Kun Wang, Yu An, Kunshan He, Tong Tong, Jie Tian · Biomedical Optics Express · 2019
Because of the optical properties of medical fluorescence images (FIs) and hardware limitations, light scattering and diffraction constrain the image quality and resolution. In contrast to device-based approaches, we developed a post-processing method for FI resolution enhancement by employing improved generative adversarial networks. To overcome the drawback of fake texture generation, we proposed total gradient loss for network training. Fine-tuning training procedure was applied to further improve the network architecture. Finally, a more agreeable network for resolution enhancement was applied to actual FIs to produce sharper and clearer boundaries than in the original images.