Improved generative adversarial networks
using the total gradient loss for resolution
enhancement of fluorescence images
Posted on 2019-08-22 - 19:23
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.
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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. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.4591613.v1
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AUTHORS (6)
CZ
Chong Zhang
KW
Kun Wang
YA
Yu An
KH
Kunshan He
TT
Tong Tong
JT
Jie Tian