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基于自适应分块和联合优化光滑l0范数的二维压缩感知算法

张小贝 唐辰 涂喜梅 陆晓刚 张琦

张小贝, 唐辰, 涂喜梅, 陆晓刚, 张琦. 基于自适应分块和联合优化光滑l0范数的二维压缩感知算法[J]. 电子与信息学报, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097
引用本文: 张小贝, 唐辰, 涂喜梅, 陆晓刚, 张琦. 基于自适应分块和联合优化光滑l0范数的二维压缩感知算法[J]. 电子与信息学报, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097
ZHANG Xiaobei, TANG Chen, TU Ximei, LU Xiaogang, ZHANG Qi. 2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l0 Norm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097
Citation: ZHANG Xiaobei, TANG Chen, TU Ximei, LU Xiaogang, ZHANG Qi. 2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l0 Norm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097

基于自适应分块和联合优化光滑l0范数的二维压缩感知算法

doi: 10.11999/JEIT221097
基金项目: 工信部民用飞机专项科研项目(MJZ-2018-S-30)
详细信息
    作者简介:

    张小贝:男,教授,研究方向为光电信息技术

    唐辰:男,硕士生,研究方向为航空图像压缩加密

    涂喜梅:女,研究员,研究方向为航空无线通信系统

    陆晓刚:男,高级工程师,研究方向为航空数据链通信系统

    张琦:男,讲师,研究方向为光电信息技术

    通讯作者:

    张小贝 xbzhang@shu.edu.com

  • 中图分类号: TN911.73

2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l0 Norm

Funds: The Ministry of Industry and Information Technology Civil Aircraft Special Research Project (MJZ-2018-S-30)
  • 摘要: 传统的压缩感知模型和重构方法,虽能有效减少数据量,但压缩和重构性能不佳,故该文提出一种基于自适应分块和联合优化光滑l0范数(SL0)的2维压缩感知算法。压缩过程利用灰度熵和四叉树算法进行自适应分块和采样率分配,同时对压缩模型改进,使用混沌循环矩阵作为测量矩阵,提升了压缩性能。重构过程基于SL0算法,采用陡峭性更高的拟合函数,结合拟牛顿法和动态迭代的方案提高重构质量和效率。该算法峰值信噪比和结构相似性指数相比现有算法平均提升了5.44 dB和21.08%,平均计算时间仅需1.59 s,表明该算法能稳定、快速地实现图像的压缩感知和精确重构,为压缩感知和图像重构提供了新方法。
  • 图  1  2D-ABCS-JOSL0算法流程图

    图  2  图像自适应分块流程

    图  3  l0近似函数对比图

    图  4  6幅测试图像

    图  5  图像自适应分块后结果

    图  6  图像单一优化算法PSNR对比

    图  7  图像单一优化算法SSIM对比

    图  8  30%压缩采样率下不同算法Lena图像结果对比

    图  9  30%压缩采样率下不同算法Barbara图像重构结果对比

    图  10  不同算法计算时间对比

    表  1  不同图像不同算法PSNR(dB)/SSIM(%))对比

    压缩采样率(%)算法PeppersLenaGoldhillBarbaraBoatMandrill平均值
    10BCS-SPL23.79/64.8623.58/64.5726.71/54.9823.12/55.1223.64/55.122.54/37.1523.65/55.30
    ABCSDI-SPL24.79/72.0924.05/73.8727.57/57.2623.97/55.5224.28/56.5420.81/36.0224.24/58.55
    2DPL27.79/86.8127.13/83.9927.05/74.4224.34/72.9623.54/73.4321.89/50.8425.29/73.74
    TV-MS-BCS-SPL28.21/87.0327.33/80.4328.21/75.8928.01/74.0424.27/74.3322.97/52.3226.50/74.01
    2D-ABCS-JOSL028.75/87.1329.50/85.8928.79/78.7628.67/80.3326.57/77.8424.38/59.7327.77/78.28
    30BCS-SPL28.54/77.0829.04/81.3530.13/73.0226.20/68.2526.65/73.4226.03/52.4727.35/70.93
    ABCSDI-SPL29.93/82.5029.92/87.1232.09/72.2530.39/70.0127.65/73.4324.54/52.5929.09/72.98
    2DPL34.24/94.3434.33/93.1630.48/87.9128.46/85.7630.48/87.9826.36/69.8830.73/86.50
    TV-MS-BCS-SPL34.32/93.1330.99/88.9433.12/83.9529.76/82.4628.87/83.3227.04/73.5630.68/84.23
    2D-ABCS-JOSL034.94/94.4935.74/94.2733.51/90.3632.58/88.1131.13/89.7127.47/79.5032.56/89.41
    50BCS-SPL31.53/84.9032.43/87.0432.85/80.0830.05/79.5428.93/82.5129.38/68.7230.20/80.47
    ABCSDI-SPL33.02/88.4533.72/92.6533.84/81.5433.12/81.1130.02/82.5026.85/70.3231.76/82.76
    2DPL36.75/95.0337.56/95.2934.01/91.2232.64/91.0434.01/93.7229.01/86.2534.00/92.26
    TV-MS-BCS-SPL36.95/96.0033.20/92.0034.84/88.4733.96/88.9132.72/90.1328.04/81.2333.29/89.46
    2D-ABCS-JOSL037.85/96.0438.61/96.0035.87/93.6735.12/92.0334.58/93.7329.94/86.9535.33/93.07
    70BCS-SPL33.81/90.7534.87/94.0234.28/91.1933.52/87.8931.89/90.4232.01/81.0232.90/89.22
    ABCSDI-SPL36.71/94.3438.12/96.1434.95/89.2636.01/88.1433.38/90.9928.55/82.3434.62/90.20
    2DPL38.63/96.5539.55/96.8836.98/94.5636.25/94.6536.22/95.1331.83/90.7536.58/94.75
    TV-MS-BCS-SPL38.10/96.0934.92/93.9136.52/93.8736.32/93.4335.93/94.8131.75/90.1235.59/93.70
    2D-ABCS-JOSL039.64/96.7440.05/96.8837.28/95.0837.21/94.8036.66/95.5432.56/91.9737.23/95.17
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-22
  • 修回日期:  2023-03-31
  • 网络出版日期:  2023-04-04
  • 刊出日期:  2023-12-26

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