基于傅立叶域光谱基复用的快拍高光谱成像
Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain
摘要：
高光谱成像是一种重要的成像工具，已经在各个领域得到了广泛的应用，但在动态场景的观测方面仍然存在局限性。在本文中，我们提出了一种快照高光谱成像技术，利用自然场景的光谱和空间稀疏性。在计算成像方案下，我们对高光谱数据立方体进行光谱降维和空间频率截断，并以低成本的方式对其进行快照。具体地说，我们调制的光谱变化的几个宽带光谱滤波器，然后将这些调制图像到不同的区域中的傅立叶域。编码后的图像在光谱和空间两个方向上压缩，最后由单色探测器采集。相应地，重建实质上是具有低计算负荷的傅立叶域提取和谱维反投影。2D传感器中的这种傅立叶光谱复用简化了编码和解码过程，并以低成本的方式捕获高光谱数据。我们证明了我们的方法的高性能的仿真数据的定量评估，并建立了一个原型系统的实验进一步验证。
英文摘要：
Hyperspectral imaging is an important tool having been applied in various fields, but still limited in observation of dynamic scenes. In this paper, we propose a snapshot hyperspectral imaging technique which exploits both spectral and spatial sparsity of natural scenes. Under the computational imaging scheme, we conduct spectral dimension reduction and spatial frequency truncation to the hyperspectral data cube and snapshot it in a low cost manner. Specifically, we modulate the spectral variations by several broadband spectral filters, and then map these modulated images into different regions in the Fourier domain. The encoded image compressed in both spectral and spatial are finally collected by a monochrome detector. Correspondingly, the reconstruction is essentially a Fourier domain extraction and spectral dimensional back projection with low computational load. This Fourier-spectral multiplexing in a 2D sensor simplifies both the encoding and decoding process, and makes hyperspectral data captured in a low cost manner. We demonstrate the high performance of our method by quantitative evaluation on simulation data and build a prototype system experimentally for further validation.
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