计算机科学
高光谱成像
旋光法
快照(计算机存储)
编码
极化(电化学)
光学成像
光学
人工智能
斯托克斯参量
数据采集
物理
光谱成像
计算机视觉
数据压缩
图像分辨率
迭代重建
图像压缩
均方误差
无损压缩
相(物质)
作者
Zhijie Lin,Weizhu Xu,Zhiwei Huang,Tao Yue,Xuemei Hu
摘要
ABSTRACT High‐dimensional light fields encode rich physical information across spectral, polarimetric, and phase dimensions, far exceeding the intensity‐only data captured by conventional sensors. Accessing these additional domains provides a transformative foundation for multidimensional sensing and intelligent perception, with wide‐ranging applications from biomedical imaging to autonomous systems. However, existing high‐dimensional light‐field imaging systems are often constrained by bulky form factors, limited imaging speed, and limited robustness. This paper presents a novel collaborative framework that integrates a disordered metasurface array with a spatial–spectral–polarization network (SSP‐Net), enabling single‐shot, high‐resolution hyperspectral and full‐Stokes polarimetric imaging. Experimental results demonstrate that the proposed system achieves high‐fidelity high‐dimensional light‐field reconstruction across the visible spectrum (450–650 nm) at a compression ratio of up to 84:1, corresponding to the 21 spectral channels and four full‐Stokes parameters from a single measurement, with a mean peak signal‐to‐noise ratio (PSNR) of 37.11 dB and a mean squared error (MSE) of 0.029 for the degree of polarization (DoP). This work establishes a compact, efficient, and high‐quality paradigm for the acquisition of multidimensional optical information.
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