Coded aperture design for hyper-spectral image recovery via Matrix Completion
Coded aperture design for hyper-spectral image recovery via Matrix Completion
复制标题
通过矩阵补全进行高光谱图像恢复的编码孔径设计
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Hoover F. Rueda
中科院分区：
文献类型：
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作者：
Tatiana Gelvez;H. Arguello;Hoover F. Rueda
光谱图像是一个三维的空间光谱集合，其中包含了场景中每个空间位置的大量光谱信息。压缩光谱成像技术（CSI）允许在二维编码投影中捕获3D场景。编码孔径快照光谱成像仪（CASSI）是一种利用CSI对单次投影的光谱图像进行感知的光学结构。与传统方法相比，CSI提高了传感速度，减少了采集数据量。然后通过求解基于1的优化问题来恢复三维场景。然而，这个问题假设场景在一些已知的标准正交基上是稀疏的。相比之下，一种称为矩阵完成（MC）的技术允许在没有这种先验知识的情况下恢复场景。MC重建算法依赖于场景的低秩结构。此外，CASSI测量估计场景的质量取决于在传感过程中使用的编码孔径模式。因此，本文提出了MC方法的最优编码孔径集设计。设计的集合是通过最大化编码孔径中半透明元素之间的距离来实现的。仿真结果表明，当使用设计的信号集时，平均增益约为5db。
A spectral image is a 3-dimensional spatio-spectral set with a large amount of spectral information for each spatial location of a scene. Compressive Spectral Imaging techniques (CSI) permit to capture the 3D scene in 2-dimensional coded projections. The Coded Aperture Snapshot Spectral Imager (CASSI) is an optical architecture to sense a spectral image in a single projection by applying CSI. CSI increases the sensing speed and reduces the amount of collected data compared to traditional methods. The 3D scene is then recovered by solving an ℓ1-based optimization problem. However, this problem assumes that the scene is sparse in some known orthonormal basis. In contrast, a technique called Matrix Completion (MC) allows the recovery of a scene without such prior knowledge. The MC reconstruction algorithms rely on a low-rank structure of the scene. Moreover, the quality of the estimated scene from CASSI measurements depends on the coded aperture patterns used in the sensing process. Therefore, this paper proposes the design of an optimal coded aperture set for the MC methodology. The designed set is attained by maximizing the distance between the translucent elements in the coded aperture. Simulations show average improvement of around 5 dB when the designed set is used.