Compressive hyperspectral imaging via adaptive sampling and dictionary learning

TL;DR

This paper introduces a novel hyperspectral imaging method combining dictionary learning and SVD for adaptive sampling, significantly enhancing reconstruction quality over traditional compressive sensing techniques.

Contribution

It presents a new sampling strategy that integrates dictionary learning with SVD to improve hyperspectral signal reconstruction.

Findings

Significant improvement over conventional compressive sensing methods

Robustness of the approach across different datasets

Enhanced reconstruction performance with matrix balancing

Abstract

In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary from training spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain the compressive measurements for reconstruction. The proposed method provides significant improvement over the conventional compressive sensing approaches. The reconstruction performance is further improved by reconditioning the sensing matrix using matrix balancing. We also demonstrate that the combination of dictionary learning and SVD is robust by applying them to different datasets.