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Found 5 papers, 1 papers with code
On the use of deep learning for phase recovery
Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing.
High-performance real-world optical computing trained by in situ model-free optimization
Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps.
NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging
Single neural networks have achieved simultaneous phase retrieval with aberration compensation and phase unwrapping in off-axis Quantitative Phase Imaging (QPI).
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
The model first trains the sentence pairs in the text, calculate similarity between sentence pairs, and fine-tunes BERT used for the named entity recognition task according to the similarity, so as to alleviate word ambiguity.
Towards new forms of particle sensing and manipulation and 3D imaging on a smartphone for healthcare applications
Close to half of the world population have smartphones, while a typical flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans.
