Fourier Ptychography Microscopy Based on Super-Resolution Adversarial Network
Laser and Optoelectronics Progress, ISSN: 1006-4125, Vol: 60, Issue: 20
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Fourier ptychography microscopy (FPM) is limited by hardware and algorithm, and its overall performance needs to be improved. To address the issues of slow imaging speed and low imaging quality of traditional FPM technology, the FPM image reconstruction approach integrated with depth learning has been widely explored. Herein, based on this, a superresolution countermeasure generation networkbased FPM model is proposed. Furthermore, global feature fusion is obtained by adding dense block connections using the original network, and a weighted loss function is used to enhance the quality of image reconstruction. The reconstruction results of the resolution plate image demonstrate that the proposed depth learning method has a better reconstruction effect and faster reconstruction speed than the conventional method.
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