计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 306-311.doi: 10.11896/j.issn.1002-137X.2018.04.052
杜秀丽,顾斌斌,胡兴,邱少明,陈波
DU Xiu-li, GU Bin-bin, HU Xing, QIU Shao-ming and CHEN Bo
摘要: 压缩采样匹配追踪(CoSaMP)算法的性能受初始支撑集选择的制约,初始支撑集选择不准确不仅影响重构精度,还会降低重构速度。针对该问题,将图像在稀疏域的结构特性引入到CoSaMP算法中,提出了支撑集相似度的概念;利用数字图像相邻行之间原子支撑集的相似性,提出了基于行间支撑集相似度的CoSaMP算法。实验结果表明,在同等采样率的条件下, 与传统的CoSaMP算法相比,所提算法在不增加算法时间复杂度的同时提高了重构质量 ,峰值信噪比提高了0.6~2.5dB。
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