针对高光谱图像相邻波段之间具有强光谱相关性的特点，为了提高高光谱图像压缩感知的重构效果，本文提出一种利用边缘信息设计动态测量率的压缩感知算法。首先，通过随机投影的分块压缩感知方法对每个图像块以固定测量率采样，重构出单波段图像作为其他波段的先验信息，并对其提取出图像边缘区域; 然后，根据每个图像块边缘信息的丰富程度来自适应分配测量值。在固定总测量数的前提下，对不同图像块分配不同的测量次数。最后，利用分配好的测量次数对其余波段进行采集和重构。仿真结果表明，在相同总测量数情况下，本文提出的动态测量算法重构出的高光谱图像质量(PSNR)与传统固定测量压缩感知策略相比提高了1~4 dB，相比较下的重构时间也减少，在成功重构高光谱图像的基础上更增强了细节处的图像质量。

Based on the strong spectral correlation between adjacent bands of hyperspectral images, we proposes a compressed sensing algorithm that uses edge information to design dynamic measurement rate to improve the reconstruction effect of compressive sensing in hyperspectral images. First, each image block is sampled at a fixed measurement rate by a random projection block-compressive sensing method, a single-band image is reconstructed as a priori information of other bands, and an image edge region is extracted therefrom; then, the measurement values are adaptively assigned according to the richness of the edge information of each image block. With a certain total number of measurements, different number of measurements is assigned to different image blocks. Finally, the remaining wave bands are collected and reconstructed with the assigned measurements. The simulation results show that under the same total number of measurements, the hyperspectral image quality(Peak Signal to Noise Ratio(PSNR)) reconstructed by the dynamic measurement algorithm proposed in this paper is 1-4 dB higher than the traditional fixed-measurement compressive sensing strategy. Moreover, the reconstruction time is also reduced, and the image quality at the detail is further enhanced based on the successful reconstruction of the hyperspectral images.