[1] 焦李成, 杨淑媛, 刘芳, 等. 压缩感知回顾与展望[J]. 电子学报, 2011, 39(7): 1651-1662.

    Jiao L C, Yang S Y, Liu F, et al. Development and prospect of compressive sensing[J]. Acta Electronica Sinica, 2011, 39(7): 1651-1662.

[2] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[3] 王与烨, 任宇琛, 陈霖宇, 等. 基于分块压缩感知理论的太赫兹波宽光束成像技术[J]. 光学学报, 2019, 39(4): 0407001.

    Wang Y Y, Ren Y C, Chen L Y, et al. Terahertz wave wide-beam imaging technology based on block compressive sensing theory[J]. Acta Optica Sinica, 2019, 39(4): 0407001.

[4] 柯钧, 张临夏, 周群. 压缩感知在光学成像领域的应用[J]. 光学学报, 2020, 40(1): 0111006.

    Ke J, Zhang L X, Zhou Q. Applications of compressive sensing in optical imaging[J]. Acta Optica Sinica, 2020, 40(1): 0111006.

[5] GanL. Block compressed sensing of natural images[C]∥2007 15th International Conference on Digital Signal Processing, July 1-4, 2007, Cardiff, UK.New York: IEEE Press, 2007: 403- 406.

[6] 王玥, 周城, 熊承义, 等. 基于纹理自适应全变分滤波的图像分块压缩感知优化算法[J]. 计算机科学, 2016, 43(2): 307-310,315.

    Wang Y, Zhou C, Xiong C Y, et al. Enhanced block compressed sensing of images based on total variation using texture information[J]. Computer Science, 2016, 43(2): 307-310,315.

[7] 杜秀丽, 张薇, 顾斌斌, 等. 基于灰度共生矩阵的图像自适应分块压缩感知方法[J]. 计算机科学, 2018, 45(8): 277-282.

    Du X L, Zhang W, Gu B B, et al. GLCM-based adaptive block compressed sensing method for image[J]. Computer Science, 2018, 45(8): 277-282.

[8] 蔡旭, 谢正光, 黄宏伟, 等. 一种自适应低采样率图像分块压缩感知算法[J]. 小型微型计算机系统, 2016, 37(3): 612-616.

    Cai X, Xie Z G, Huang H W, et al. An adaptive reconstruction algorithm for image block compressed sensing under low sampling rate[J]. Journal of Chinese Computer Systems, 2016, 37(3): 612-616.

[9] 曹玉强, 柏森, 曹明武. 图像自适应分块的压缩感知采样算法[J]. 中国图象图形学报, 2016, 21(4): 416-424.

    Cao Y Q, Bai S, Cao M W. Image compression sampling based on adaptive block compressed sensing[J]. Journal of Image and Graphics, 2016, 21(4): 416-424.

[10] Fowler JE, MunS, Tramel EW. Multiscale block compressed sensing with smoothed projected Landweber reconstruction[C]∥2011 19th European Signal Processing Conference, August 29 - September 2, 2011, Barcelona, Spain.New York: IEEE Press, 2011: 564- 568.

[11] 李玉, 赵瑞珍, 张凤珍, 等. 边缘和方向估计的自适应多尺度分块压缩感知算法[J]. 信号处理, 2015, 31(4): 407-413.

    Li Y, Zhao R Z, Zhang F Z, et al. Adaptive multi-scale block compressed sensing algorithm based on edge and direction estimation[J]. Journal of Signal Processing, 2015, 31(4): 407-413.

[12] 程德强, 邵丽蓉, 李岩, 等. 多尺度分块的自适应采样率压缩感知算法[J]. 激光与光电子学进展, 2019, 56(3): 031005.

    Cheng D Q, Shao L R, Li Y, et al. Multi-scale block adaptive sampling rate compression sensing algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031005.

[13] 李如春, 程云霄, 李林, 等. 自适应分块压缩感知的图像压缩算法[J]. 浙江工业大学学报, 2018, 46(4): 392-395,406.

    Li R C, Cheng Y X, Li L, et al. An adaptive blocking compressive sensing for image compression[J]. Journal of Zhejiang University of Technology, 2018, 46(4): 392-395,406.

[14] MunS, Fowler JE. Block compressed sensing of images using directional transforms[C]∥2009 16th IEEE International Conference on Image Processing (ICIP), November 7-10, 2009, Cairo, Egypt.New York: IEEE Press, 2009: 3021- 3024.

[15] 杨龙飞. 基于局部二值模式和灰度共生矩阵的纹理特征提取技术研究[D]. 兰州: 兰州大学, 2016: 42- 43.

    Yang LF. The study on texture feature extraction method based on LBP and GLCM[D]. Lanzhou: Lanzhou University, 2016: 42- 43.

[16] 刘芳, 武娇, 杨淑媛, 等. 结构化压缩感知研究进展[J]. 自动化学报, 2013, 39(12): 1980-1995.

    Liu F, Wu J, Yang S Y, et al. Research advances on structured compressive sensing[J]. Acta Automatica Sinica, 2013, 39(12): 1980-1995.

[17] 李红兵. 图像压缩感知中结构化观测矩阵和高性能重构算法研究[D]. 重庆: 重庆邮电大学, 2017: 28- 33.

    Li HB. Research on structured measurement matrix and high performance reconstruction algorithm in image compressive sensing[D]. Chongqing: Chongqing University of Posts and Telecommunications, 2017: 28- 33.

[18] Sendur L, Selesnick I W. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J]. IEEE Transactions on Signal Processing, 2002, 50(11): 2744-2756.

[19] 张杰. 基于压缩感知的天文图像压缩及去噪重建算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018: 28- 32.

    ZhangJ. Astronomical image compression and denoising reconstruction algorithms based on compressed sensing[D]. Harbin: Harbin Institute of Technology, 2018: 28- 32.