ArticleBiomedical optics express2019
Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Advances in Surgical Management of Malignant Tumors of the Distal Tibia.Current treatment options in oncology · 2025Review
- Blood perfusion assessment by near-infrared fluorescence angiography of epiploic appendages in prevention of anastomotic leakage after laparoscopic intersphincteric resection for ultra-low rectal cancer: a case-matched study.Surgical endoscopy · 2024Article
- A review of the application of machine learning in molecular imaging.Annals of translational medicine · 2021Review
- Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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