CAJ | 학술논문

目前，标准的CS重构算法仅利用信号和图像在小波变换下的稀疏先验信息，而并没有利用变换系数具有的结构化特性。为了能够快速精确地重建原始信号，将结构化稀疏模型与SP算法、CoSaMP算法相结合，提出了压缩感知重构的改进算法。另外，将基于双树复小波变换的系数结构模型融入上述算法，进一步提高重构性能。实验结果表明，所提出的算法可获得更高的图像重建质量。
목전，표준적CS중구산법부이용신호화도상재소파변환하적희소선험신식，이병몰유이용변환계수구유적결구화특성。위료능구쾌속정학지중건원시신호，장결구화희소모형여SP산법、CoSaMP산법상결합，제출료압축감지중구적개진산법。령외，장기우쌍수복소파변환적계수결구모형융입상술산법，진일보제고중구성능。실험결과표명，소제출적산법가획득경고적도상중건질량。
Recently, normal recovery algorithms for CS only use signal and image sparse priors under wavelet, make no use of the tree structure priors. In order to reconstruct the original signal quickly and accurately, this paper brings the tree structure sparse model into SP algorithm , CoSaMP-algorithm and gets the improved recovery algorithm for compressed sensing. Combin-ing with structured sparse model and dual-tree complex wavelet transform, a new recovery algorithm for CS is proposed. The simulated results show that the algorithm can achieve higher reconstructed image performance.