Abstract
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.
Abstract (translated)
超分辨率成像包括 excessive data，因此对数据处理、存储和传输带来显著挑战。在超分辨率成像领域，压缩感知是一种用于压缩大量数据的压缩技术。本文研究了在超分辨率成像中恢复压缩后超分辨率图像2.5倍压缩的情况。在超分辨率图像恢复方面，对凸FISTA/ADMM的准确性和性能进行了比较研究，还包括贪婪梯度OMP/BIHT/CoSaMP恢复算法。结果显示，这些算法成功地恢复压缩后的数据，然而，与其它算法相比，gOMP算法在准确性和恢复速度方面具有优越性，但数据的不确定稀疏水平对算法恢复效果的影响较大。
URL
https://arxiv.org/abs/2401.14762
