干涉高光谱图像是一类特殊的图像源, 其海量数据导致很难在有限带宽信道上传输。传统的方法是对数据进行压缩, 然后进行编码传输。但是压缩后的数据还是很大, 给数据的传输和存储带来很大困难, 而压缩感知技术可以很好地解决该类图像在传输时的问题。本文在压缩感知原有算法的基础上提出了更适用于干涉高光谱图像的基于自适应阈值的正交匹配追踪算法 (ATROMP), 该算法首先采用分块处理, 然后挑选出干涉条纹块。由于竖直干涉条纹具有较强的单方向特性, 水平全变分值较大。因此本文根据水平全变分值提取出图像中的干涉条纹, 进行自适应采样。然后采用一个自适应阈值来代替正则正交匹配追踪(ROMP)算法中的二次选取, 采用自适应阈值不仅可以保障每次选取的原子的相关性足够高, 而且每次可以适当地选取多个原子保证足够的循环次数, 避免了后续匹配度更高原子的遗漏。相比于传统 ROMP算法, 大量实验数据表明本文方法稀疏重建的精度可以得到明显的提高。

Abstract: Interferometric hyperspectral image is a special kind of image source, which contains massive data and is difficult to transmit on a limited bandwidth channel. The traditional method is to compress the data and then encode the transmission. However, the compressed data is still very large, which brings great difficulties to the transmission and storage of data. Nevertheless, the compressed sensing technology can solve this problem well. Based on the original algorithm of compressed sensing, this paper proposes an adaptive threshold-based orthogonal matching pursuit algorithm (ATROMP) which is more suitable for interfering hyperspectral images. The algorithm first uses block processing and then selects the interference fringes. Because the vertical interference fringes have strong unidirectional characteristics, the total variation of the level is larger.Therefore, the interference fringes in the images are extracted from the horizontal total variation values for adaptive sampling. Then, an adaptive threshold is used in this paper to replace the quadratic selection in the ROMP algorithm. Using an adaptive threshold can not only ensure that the atomicity of each selected atom is sufficiently high, but also that multiple atoms can be properly selected each time to ensure sufficient number of cycles, to avoid the follow-up higher degree of atom missing. Compared with the traditional ROMP algorithm, a large amount of experimental data show that the sparse reconstruction ac- curacy of the method can be significantly improved.