Two-fold resolution increase and all-depth linearization using a neural network

Krzysztof A. Maliszewski, Sylwia M. Kolenderska
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Abstract

A neural network is proposed as a much better performing alternative to Fourier transformation. It processes raw OCT spectra into A-scans with twice better nominal axial resolution which remains intact at all depths even for an uncalibrated spectrometer and uncompensated chromatic dispersion.
两倍的分辨率增加和全深度线性化使用神经网络
神经网络被认为是一种比傅里叶变换性能更好的替代方法。它将原始OCT光谱处理成具有两倍更好的标称轴向分辨率的a扫描,即使对于未校准的光谱仪和未补偿的色散,也可以在所有深度保持完整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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