He Tianyu, Ren Wenyi, Feng Yang, Yu Ruoning, Wu Dan, Zhang Rui, Cai Yanan, Xie Yingge, Wang Jian
Opt Express. 2023 Sep 25;31(20):33387-33400. doi: 10.1364/OE.502690.
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.
近年来，多维光学传感技术的应用越来越普遍，比如光谱光场（SLF）成像仪。SLF传感器以一维光谱数据、二维空间数据和二维角度测量的形式提供信息。空间光谱和角度数据在从计算机视觉到显微镜学等各种领域都至关重要。使用SLF传感器需要分光镜或昂贵的相机阵列。本文描述了一种基于低成本RGB光场相机的压缩快照SLF成像方法。受压缩感知范式的启发，通过利用具有多头自注意力和无参数傅里叶变换模块的U形神经网络所提出的网络，可以从RGB光场相机的测量中重建四维SLF。该方法能够在400至700nm的光谱范围内采集光谱分辨率为10nm、角度分辨率为9×9且空间分辨率为622×432的图像。它为我们提供了一种实现低成本SLF成像的替代方法。
