高光谱图像压缩感知投影与复合正则重构

 压缩感知理论提供了一种新的数据获取和压缩思路,能有效地把计算负担从编码端转移到解码端。高光谱数据具备数据稀疏性、空间相关性和谱间相关性,结合这3类先验知识,提出了一种基于复合正则化的高光谱图像压缩感知投影与重构方法。该方法的编码端只需要一个简单的投影操作;在重构算法实现中,基于变量分裂的思想,把具备多个正则项的优化问题转化成多个简单的优化问题,并用迭代方式求解。实验结果表明,本文算法在高光谱图像重构上能获得更高的峰值信噪比和更好的重构效果。该方法具备极低的编码复杂度,适用于资源受限的机载和星载高光谱成像平台。

Compressed Sensing Projection and Compound Regularizer Reconstruction for Hyperspectral Images

Compressed sensing has proposed a new mechanism for data acquisition and compression,which can shift heavy computational loads from encoders to decoders.The hyperspectral images have sparse/compressible representations on some orthonormal bases and are of spectral and spatial correlations.According to the prior information of hyperspectral images,a novel hyperspectral compressed sensing projection and reconstruction method via compound regularizers is proposed.At the encoder,it only needs a simple projection.In the Implementation of the reconstruction algorithm,the problem of compound regularizers is turned into dealing with a few simple optimization problems by applying the variable-splitting method and is solved by iteration.Experimental results show that the proposed algorithm is able to reconstruct the hyperspectral images more efficiently than the current algorithms.Our method has very low decoding complexity and it is suitable for severely resource-constrained spaceborne and airborne remote sensing platforms.