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基于压缩感知理论的图像优化技术

王钢 周若飞 邹昳琨

王钢, 周若飞, 邹昳琨. 基于压缩感知理论的图像优化技术[J]. 电子与信息学报, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669
引用本文: 王钢, 周若飞, 邹昳琨. 基于压缩感知理论的图像优化技术[J]. 电子与信息学报, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669
Gang WANG, Ruofei ZHOU, Yikun ZOU. Research on Image Optimization Technology Based on Compressed Sensing[J]. Journal of Electronics and Information Technology, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669
Citation: Gang WANG, Ruofei ZHOU, Yikun ZOU. Research on Image Optimization Technology Based on Compressed Sensing[J]. Journal of Electronics and Information Technology, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669

基于压缩感知理论的图像优化技术

doi: 10.11999/JEIT190669
基金项目: 国家自然科学基金(61671184, 61401120),国家科技重大专项(2015ZX03001041)
详细信息
    作者简介:

    王钢:男,1962年生,教授,博士生导师,主要研究方向为数据通信、物理层网络编码、通信网理论与技术

    周若飞:男,1989年生,博士生,研究方向为压缩感知与图像处理、压缩感知与网络编码

    邹昳琨:男,1992年生,博士生,研究方向为多无人机通信网络性能优化

    通讯作者:

    王钢 gwang51@hit.edu.cn

  • 中图分类号: TN911.73

Research on Image Optimization Technology Based on Compressed Sensing

Funds: The National Natural Science Foundation of China (61671184, 61401120), The National Science and Technology Major Project (2015ZX03001041)
  • 摘要: 压缩感知(CS)理论是目前信息工程相关领域研究的前沿热点之一。它打破了传统的奈奎斯特采样定理,相比于其要求的最小采样频率,CS理论证明了能够从更低数目的采样中以高概率完整地恢复原始信号,在保证信息特征不丢失的前提下节省了数据采集和处理的时间成本。压缩感知理论本质上可以视为处理线性信号恢复问题的工具,因此在求解信号和图像的逆问题上有着显而易见的优势。图像退化问题便是其中之一,恢复相应的高质量图像的过程即为图像优化。为推动压缩感知理论的学术研究与实际应用,该文介绍了其基本原理与方法。根据图像优化技术的现存研究工作,分别从去噪、去模糊和超分辨三大主流方面研究了基于CS理论的优化技术。最后探讨了所面临的问题和挑战,分析了未来的发展趋势,为将来研究工作的展开提供借鉴与帮助。
  • 图  1  压缩感知理论的主要内容

    图  2  基于Wavelet和Curvelet去噪效果直观视觉对比

    图  3  基于CS图像去去噪技术的应用

    图  4  基于CS图像模糊技术的应用

    图  5  基于多帧CS图像超分辨结果

    图  6  基于CS图像超分辨技术的应用

    表  1  基于小波方法和基于曲波方法对比

    评价指标PSNR(dB)MSEMAE
    小波方法22.36289.1613.52
    曲波方法22.97254.5212.97
    下载: 导出CSV

    表  2  主流稀疏去噪方法PSNR对比(dB)

    去噪算法BM3DLSSCNCSRSSC-GSM
    Monarch图像32.4632.1532.3432.52
    Barbara图像33.2732.9633.0233.32
    Straw图像29.1328.9529.1329.16
    下载: 导出CSV

    表  3  多种超分辨方法的信息熵与平均梯度对比

    超分辨算法原始图像Bicubic插值单帧CS多帧CS
    信息熵6.1626.4736.4876.532
    平均梯度4.3553.9514.9865.282
    下载: 导出CSV
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    出版历程
    • 收稿日期:  2019-09-02
    • 修回日期:  2019-11-19
    • 网络出版日期:  2019-11-28
    • 刊出日期:  2020-01-21

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