Spectral imaging with deep learning.

Longqian Huang, Ruichen Luo, Xu Liu, Xiang Hao
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引用次数: 0

Abstract

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational spectral imaging methods have resorted to computation power for reduced system volume, but still endure long computation time for iterative spectral reconstructions. Recently, deep learning techniques are introduced into computational spectral imaging, witnessing fast reconstruction speed, great reconstruction quality, and the potential to drastically reduce the system volume. In this article, we review state-of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we've also organized publicly available spectral datasets.

利用深度学习进行光谱成像
光谱成像的目标是捕捉目标的光谱特征。传统的光谱成像扫描方法存在系统体积大、大场景图像采集速度低的问题。相比之下,计算光谱成像方法借助计算能力缩小了系统体积,但仍需花费较长的计算时间进行迭代光谱重建。最近,深度学习技术被引入到计算光谱成像中,其重建速度快、重建质量高,并有可能大幅减少系统体积。本文回顾了最先进的深度学习计算光谱成像方法。根据用于编码的不同光特性,这些方法又分为振幅编码、相位编码和波长编码方法。为了促进未来的研究,我们还整理了公开可用的光谱数据集。
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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