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

Published on 2019-08-22T19:23:04Z (GMT) by
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.

Cite this collection

Zhang, Chong; Wang, Kun; An, Yu; He, Kunshan; Tong, Tong; Tian, Jie (2019): Improved generative adversarial networks

using the total gradient loss for resolution

enhancement of fluorescence images. The Optical Society. Collection.