• ISSN 1673-5722
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结合非下采样剪切波变换的BM3D去除图像噪声方法研究

樊华 王文旭 李晓阳 陈睿 韩贞辉

樊华,王文旭,李晓阳,陈睿,韩贞辉,2023. 结合非下采样剪切波变换的BM3D去除图像噪声方法研究. 震灾防御技术,18(3):651−662. doi:10.11899/zzfy20230322. doi: 10.11899/zzfy20230322
引用本文: 樊华,王文旭,李晓阳,陈睿,韩贞辉,2023. 结合非下采样剪切波变换的BM3D去除图像噪声方法研究. 震灾防御技术,18(3):651−662. doi:10.11899/zzfy20230322. doi: 10.11899/zzfy20230322
Fan Hua, Wang Wenxu, Li Xiaoyang, Chen Rui, Han Zhenhui. BM3D Method Combined with Non-subsampled Shearlet Transform for Removing Image Noise[J]. Technology for Earthquake Disaster Prevention, 2023, 18(3): 651-662. doi: 10.11899/zzfy20230322
Citation: Fan Hua, Wang Wenxu, Li Xiaoyang, Chen Rui, Han Zhenhui. BM3D Method Combined with Non-subsampled Shearlet Transform for Removing Image Noise[J]. Technology for Earthquake Disaster Prevention, 2023, 18(3): 651-662. doi: 10.11899/zzfy20230322

结合非下采样剪切波变换的BM3D去除图像噪声方法研究

doi: 10.11899/zzfy20230322
基金项目: 中国地震局地震应急青年重点任务(CEA_EDEM-202111);河南省青年人才托举工程项目(2022HYTP028);中国地震局地震应急与信息青年重点任务(CEA_EDEM-202315)
详细信息
    作者简介:

    樊华,女,生于1988年。工程师。主要从事防震减灾公共服务、震害防御工作。E-mail:867877458@qq.com

BM3D Method Combined with Non-subsampled Shearlet Transform for Removing Image Noise

  • 摘要: 强噪声图像去噪一直是图像处理技术应用领域研究的热点,为进一步提高强噪声图像的去噪质量和对图像边缘的保护,针对三维块匹配(Block Matching 3D,BM3D)方法对强噪声图像去噪效果不佳及图像线状奇异性(如边缘)缺乏最优表示的问题,提出了基于二维非下采样剪切波变换(Non-subsampled Shearlet Transform,NSST)和BM3D的组合去噪方法。该方法首先对含噪图像进行NSST正变换,得到不同尺度和不同方向高频子带的剪切波系数;然后对每个尺度不同方向的高频子带求取贝叶斯阈值,并利用渐进半软阈值函数对各高频子带进行去噪;最后对低频子带和各去噪高频子带进行NSST逆变换,得到去噪结果。将去噪图像作为BM3D中基础估计阶段的预滤波,能够进一步提高基础估计阶段分组的正确性,为BM3D去噪奠定良好基础。组合去噪方法结合了NSST与BM3D的各自优势,仿真试验结果表明,对于低噪声图像,本方法和BM3D方法去噪效果相同,略优于非局部平均法;对于强噪声卫星影像,本方法去噪效果优于BM3D和非局部平均法。
  • 图  1  频域自适应锥形划分

    Figure  1.  Adaptive tapered subgraph in frequency domain

    图  2  NSST三级分解示意

    Figure  2.  Schematic diagram of tertiary decomposition of NSST

    图  3  NSST去噪步骤示意

    Figure  3.  NSST denoising procedure diagram

    图  4  ST-BM3D算法流程

    Figure  4.  BM3D denoising process combined with NSST transform

    图  5  低噪声图像去噪效果对比

    Figure  5.  Contrast of weak noise images denoising effect

    图  6  强噪声图像去噪效果对比

    Figure  6.  Contrast of strong noise images denoising effect

    图  7  强噪声无人机图像去噪效果对比

    Figure  7.  Comparison of strong noise UAV image denoising

    图  8  强噪声卫星影像去噪效果对比

    Figure  8.  Comparison of strong noise satellite image denoising

    表  1  去噪方法性能评价结果

    Table  1.   Performance evaluation table of denoising method

    图像名称Lena图像点线面高对比度图像无人机图像卫星影像
    噪声值(低高斯噪声标准差30 dB)(强高斯噪声标准差100 dB)(强高斯噪声标准差100 dB)(强高斯噪声标准差100 dB)
    评价参数PSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
    NL means方法25.310.9719.050.2716.330.4215.780.59
    BM3D方法28.560.9823.940.4322.070.6021.320.74
    本方法28.610.9826.050.7523.030.7122.420.80
    下载: 导出CSV
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  • 收稿日期:  2022-07-26
  • 刊出日期:  2023-08-31

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