[1] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[2] DAI Y X, ZHUANG P X. Compressed sensing MRI via a multi-scale dilated residual convolution network[J]. Magnetic Resonance Imaging, 2019,63:93-104.
[3] KOOCHAKZADEH A, MALEK-MOHAMMADI M, BABAIE-ZADEH M, et al. Multi-antenna assisted spectrum sensing in spatially correlated noise environments[J]. Signal Processing, 2015,108(3):69-76.
[4] 丁晖,赵海丞,刘家强,等. 基于压缩感知的电力设备状态感知技术[J]. 高电压技术, 2020,46(6):1877-1885.
[5] LOPES M E. Unknown sparsity in compressed sensing: Denoising and inference[J]. IEEE Transactions on Information Theory, 2016,62(9):5145-5166.
[6] CHEN W S, YOU J, CHEN B, et al. A sparse representation and dictionary learning based algorithm for image restoration in the presence of rician noise[J]. Neurocomputing, 2018,286:130-140.
[7] JIE G, BIN S, YING H, et al. A survey on compressed sensing in vehicular infotainment systems[J]. IEEE Communications Surveys and Tutorials, 2017,19(4):2662-2680.
[8] MOHIMANI H, BABAIE Z M, JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed L0 norm[J]. IEEE Transactions on Signal Processing, 2009,57(1):289-301.
[9] MALEK M M, BABAIE M, KOOCHAKZADEH A, et al. Successive concave sparsity approximation for compressed sensing[J]. IEEE Transactions on Signal Processing, 2016,64(21):5657-5671.
[10]周洁容,李海洋,凌军,等. 基于非凸复合函数的稀疏信号恢复算法[J]. 自动化学报, 2020,48(1):1-12.
[11]MOMENNEZHAD A. Matching pursuit algorithm for enhancing EEG signal quality and increasing the accuracy and efficiency of emotion recognition[J]. Biomedizinische Technik, 2020,65(4):393-404.
[12]WEN J M, ZHOU Z C, WANG J, et al. A sharp condition for exact support recovery with orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing, 2017,65(6):1370-1382.
[13]ZHU H F, CHEN W, WU Y P. Efficient implementations for orthogonal matching pursuit[J]. Electronics, 2020,9(9):1507.
[14]LU D X, SUN G L, LI Z Z, et al. Improved CoSaMP reconstruction algorithm based on residual update[J]. Journal of Computer and Communications, 2019,7(6):6-14.
[15]HUANG F, TAO J, XIANG Y, et al. Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL[J]. Journal of Systems Architecture, 2016,72(C):51-60.
[16]KIM K S, CHUNG S Y. Greedy subspace pursuit for joint sparse recovery[J]. Journal of Computational and Applied Mathematics, 2016,352:308-327.
[17]WANG J, KWON S, LI P, et al. Recovery of sparse signals via generalized orthogonal matching pursuit: A new analysis[J]. IEEE Transactions on Signal Processing, 2016,64(4):1076-1089.
[18]ZHAO L Q, LIU Y L. A new generalized orthogonal matching pursuit method[J]. Journal of Electrical and Computer Engineering, 2017,2017(2):1-7.
[19]LI G, LIU Q H, GU Y T. Rigorous restricted isometry property of low-dimensional subspaces[J]. Applied and Computational Harmonic Analysis, 2020,49(2):608-635.
[20]SHEN Y, LI B, PAN W L, et al. Analysis of generalized orthogonal matching pursuit using restricted isometry constant[J]. Electronics Letters, 2014,50(14):1020-1022.
[21]BARANIUK R G. Compressive sensing[J]. IEEE Signal processing magazine, 2007,24(4):118-121.
[22]张晓东,董唯光,汤旻安,等. 压缩感知中基于广义Jaccard系数的GOMP重构算法[J]. 山东大学学报(理学版), 2017,52(11):23-28.
[23]王坤,吴一鸣,诸葛晶昌,等. 一种基于广义Jaccard系数的MsGOMP红外图像去噪算法[J]. 红外技术, 2019,41(6):577-584.
[24]张宇,刘雨东,计钊. 向量相似度测度方法[J]. 声学技术, 2009,28(4):532-536.
|