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基于深度学习的显微技术
Deep Learning Microscopy
## Deep Learning Microscopy ### Introduction Deep learning is a learning model of machine learning, which uses very deep structure of multi-layer neural network activated by non-linear functions to analyze signals and data. Convolutional neural network (CNN), taking advantage of local connections and shared weights, has made major advances in computer-vision community. Recently, deep learning algorithm has also been applied in cross-modelity microscopy, phase recovery, holographic image reconstruction and so on. So we are also inspired by the powerful deep learning and previous work on microscopy and proposed several deep learning-based microscopy algorithm. ### Highlights   #### Deep Convolutional Neural Network for Tomographic Phase Microscopy - GPU-based implementation of deep CNN to model the propagation of light beam - Layer-wise propagation model based on beam propagation method - Complex-valued neural network implemented on Tensorflow & Keras - ℓ<sub>1</sub>-norm constrained loss function - ℓ<sub>1</sub>-norm data fidelity term & ℓ<sub>1</sub>-norm gradient-domain regularizer > Qiao, H., Wu, J., Li, X., Shoreh, M. H., Fan, J., & Dai, Q. (2018). [GPU-based deep convolutional neural network for tomographic phase microscopy with l1 fitting and regularization](https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-23/issue-6/066003/GPU-based-deep-convolutional-neural-network-for-tomographic-phase-microscopy/10.1117/1.JBO.23.6.066003.full?SSO=1). Journal of biomedical optics, 23(6), 066003.
