快照(计算机存储)
计算机科学
RGB颜色模型
光谱成像
光学
人工智能
计算机视觉
图像分辨率
压缩传感
光场
视野
遥感
物理
地质学
操作系统
作者
Tianyu He,Wenyi Ren,Yang Feng,Ruoning Yu,Dan Wu,Rui Zhang,Yanan Cai,Yingge Xie,Jian Wang
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2023-09-08
卷期号:31 (20): 33387-33387
被引量:1
摘要
The application of multidimensional optical sensing technologies, such as the spectral light field (SLF) imager, has become increasingly common in recent years. The SLF sensors provide information in the form of one-dimensional spectral data, two-dimensional spatial data, and two-dimensional angular measurements. Spatial-spectral and angular data are essential in a variety of fields, from computer vision to microscopy. Beam-splitters or expensive camera arrays are required for the usage of SLF sensors. The paper describes a low-cost RGB light field camera-based compressed snapshot SLF imaging method. Inspired by the compressive sensing paradigm, the four dimensional SLF can be reconstructed from a measurement of an RGB light field camera via a network which is proposed by utilizing a U-shaped neural network with multi-head self-attention and unparameterized Fourier transform modules. This method is capable of gathering images with a spectral resolution of 10 nm, angular resolution of 9 × 9, and spatial resolution of 622 × 432 within the spectral range of 400 to 700 nm. It provides us an alternative approach to implement the low cost SLF imaging.
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