Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images.

ABSTRACT: 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.

SUBMITTER: Zhang C 

PROVIDER: S-EPMC6757480 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature