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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

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 shu
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 shu

基于压缩感知理论的图像优化技术

    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
  • 基金项目: 国家自然科学基金(61671184, 61401120),国家科技重大专项(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。

English

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  • 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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文章相关
  • 通讯作者:  王钢, gwang51@hit.edu.cn
  • 收稿日期:  2019-09-02
  • 录用日期:  2019-11-19
  • 网络出版日期:  2019-11-28
  • 刊出日期:  2020-01-01
通讯作者: 陈斌, bchen63@163.com
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