SVD熵表示编码衍射广义重构精度

Altai Perry, Xiaojing Weng, Baurzhan Muminov, L. Vuong
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引用次数: 1

摘要

本文利用奇异值分解熵(hsvd)测量图像跨度,研究了浅层神经网络编码衍射。高h SVD合成训练图像学习速度较慢;它们的广义模型一般具有较高的重建精度。
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SVD Entropy Indicates Coded Diffraction Generalized Reconstruction Accuracy
We study encoded diffraction with shallow neural networks using singular value decomposition entropy (H SVD ) to measure image span. Higher-H SVD synthetic training images are learned more slowly; their generalized models generally attain higher reconstruction accuracy.
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