Superior phase recovery and hologram reconstruction using a deep neural network

Deep learning has achieved benchmark results for various imaging tasks, including holographic microscopy, where an essential step is to recover the phase information of samples using intensity-only measurements. By training on well-designed datasets, deep neural networks have proven to outperform classical phase retrieval and hologram reconstruction algorithms in terms of accuracy and computational efficiency. However, model generalization, which refers to extending the neural networks' capabilities to new types of samples never seen during the training, remains a challenge for existing …

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