论文标题
通过双摄像头压缩高光谱成像的非著作融合来快速高光谱图像恢复
Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging
论文作者
论文摘要
编码的光圈快照光谱成像（CASSI）是一种使用单个编码的二维（2D）测量值捕获三维高光谱图像（HSI）的有前途的技术，其中使用算法来执行逆问题。由于性质不足，已利用各种正规化器从2D测量中重建3D数据。不幸的是，准确性和计算复杂性不满意。一种可行的解决方案是利用其他信息，例如CASSI中的RGB测量。考虑到CASSI和RGB的组合测量，在本文中，我们为HSI重建提出了一个新的融合模型。我们研究了由光谱基和空间系数组成的HSI的频谱低级特性。具体而言，使用RGB测量来估计系数，同时采用了CASSI测量来提供正交的光谱基础。我们进一步提出了一种补丁处理策略，以增强HSI的频谱低率特性。提出的模型既不需要非本地处理或迭代，也不需要RGB检测器的光谱传感矩阵。对模拟和真实HSI数据集进行的广泛实验表明，我们提出的方法的表现不仅优于先前的质量，而且还可以加快重建超过5000倍的速度。
Coded aperture snapshot spectral imaging (CASSI) is a promising technique to capture the three-dimensional hyperspectral image (HSI) using a single coded two-dimensional (2D) measurement, in which algorithms are used to perform the inverse problem. Due to the ill-posed nature, various regularizers have been exploited to reconstruct the 3D data from the 2D measurement. Unfortunately, the accuracy and computational complexity are unsatisfied. One feasible solution is to utilize additional information such as the RGB measurement in CASSI. Considering the combined CASSI and RGB measurement, in this paper, we propose a new fusion model for the HSI reconstruction. We investigate the spectral low-rank property of HSI composed of a spectral basis and spatial coefficients. Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis. We further propose a patch processing strategy to enhance the spectral low-rank property of HSI. The proposed model neither requires non-local processing or iteration, nor the spectral sensing matrix of the RGB detector. Extensive experiments on both simulated and real HSI dataset demonstrate that our proposed method outperforms previous state-of-the-art not only in quality but also speeds up the reconstruction more than 5000 times.
