Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images 2.5x compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover.

翻译：高光谱成像数据量过大，给数据处理、存储和传输带来了重大挑战。压缩感知技术已被用于高光谱成像领域以压缩大量数据。本文研究了高光谱图像在2.5倍压缩下的重建问题，针对凸优化类FISTA/ADMM算法以及贪婪类gOMP/BIHT/CoSaMP重建算法，从精度和性能两方面进行了比较分析。结果表明，这些算法均能成功恢复压缩数据，但gOMP算法在精度和恢复速度上优于其他算法，代价是高度依赖待恢复数据的未知稀疏度水平。