1(Department of Electronic Engineering,Xi′an Aeronautical University,Xi′an 710077,China)2(College of Physics & Electronic Engineering,Xianyang Normal University,Xianyang 712000,China)
Abstract：Utilizing orthogonal matching pursuit(OMP)algorithm could achieve the compressed sensing(CS)reconstruction of hyperspectral image(HSI)based on redundant dictionary.Due to the large number bands of HSI,the high computational complexity of OMP algorithm could not meet the real-time processing requirements.Aiming at this defect,a fast OMP reconstruction algorithm based on the idea of particle swarm optimization(PSO)is proposed(fastPSO_OMP).PSO algorithm which is more powerful in searching for local optimal solution is applied to optimize the matching process of OMP.In addition,the Hermitian inversion lemma is explored to update the residual in an iterative way,leading to improve the efficiency of the algorithm.Experimental results carried on the compressed sensing reconstruction of HSI demonstrate that the computational complexity of proposed algorithm could be reduced and the computational efficiency is increased by 18 times while keeping high reconstruction accuracy.
2019, Vol. 40 
