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New physics-informed neural network for unive - EurekAlert
To address the limitations of current computational super-resolution microscopy, a team of researchers at Zhejiang University has introduced a novel deep-physics-informed sparsity framework that significantly enhances structural fidelity and universality. This innovative method integrates physical imaging models, prior knowledge, a back-end optimization algorithm and deep learning. It can enhance the physical resolution by at least 1.67 times across diverse imaging modalities without requiring additional training or parameter adjustments. The research was published Feb. 26 in Intelligent Computing, a Science Partner Journal.
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