Numerical dark-field imaging using deep-learning
| Autor: | Zhang Meng, Liqi Ding, Giancarlo Pedrini, Caojin Yuan, Shouping Nie, Jun Ma, Shaotong Feng, Fangjian Xing |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning Image registration Image processing Astrophysics::Cosmology and Extragalactic Astrophysics 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Dark field microscopy Convolutional neural network Atomic and Molecular Physics and Optics Image (mathematics) 010309 optics Optics Optical imaging 0103 physical sciences Microscopy Computer vision Artificial intelligence 0210 nano-technology business |
| Zdroj: | Optics Express. 28:34266 |
| ISSN: | 1094-4087 |
| DOI: | 10.1364/oe.401786 |
| Popis: | Dark-field microscopy is a powerful technique for enhancing the imaging resolution and contrast of small unstained samples. In this study, we report a method based on end-to-end convolutional neural network to reconstruct high-resolution dark-field images from low-resolution bright-field images. The relation between bright- and dark-field which was difficult to deduce theoretically can be obtained by training the corresponding network. The training data, namely the matched bright- and dark-field images of the same object view, are simultaneously obtained by a special designed multiplexed image system. Since the image registration work which is the key step in data preparation is not needed, the manual error can be largely avoided. After training, a high-resolution numerical dark-field image is generated from a conventional bright-field image as the input of this network. We validated the method by the resolution test target and quantitative analysis of the reconstructed numerical dark-field images of biological tissues. The experimental results show that the proposed learning-based method can realize the conversion from bright-field image to dark-field image, so that can efficiently achieve high-resolution numerical dark-field imaging. The proposed network is universal for different kinds of samples. In addition, we also verify that the proposed method has good anti-noise performance and is not affected by the unstable factors caused by experiment setup. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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