Name
Abstract | ||
|---|---|---|
We for the first time combine generated adversarial network (GAN) with wide-field light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. This capacity has been adequately demonstrated by imaging various types of samples, such as USAF resolution target, human pathological slides and fluorescence-labelled fibroblast cells. Their gigapixel, multi-color reconstructions verify a successful GAN-based single image super-resolution procedure. Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV (about 95 mm^2), high-resolution (about 1.7 {mu}m) image at a high speed (in 1 second). As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput, high-resolution microscopy that is originally coupled to the physical limitation of systemu0027s optics. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Image and Video Processing | Field of view,Computer vision,Computer science,Microscope,Artificial intelligence,Throughput,Microscopy,Deep learning |
DocType | Volume | Citations |
Journal | abs/1801.07330 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Zhang | 1 | 207 | 58.59 |
| Xinlin Xie | 2 | 0 | 0.34 |
| Chunyu Fang | 3 | 0 | 0.34 |
| Yicong Yang | 4 | 0 | 0.34 |
| Di Jin | 5 | 2 | 4.09 |
| Peng Fei | 6 | 0 | 0.68 |