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稀疏阈值的超分辨率图像重建

何阳 黄玮 王新华 郝建坤

何阳, 黄玮, 王新华, 郝建坤. 稀疏阈值的超分辨率图像重建[J]. 中国光学, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532
引用本文: 何阳, 黄玮, 王新华, 郝建坤. 稀疏阈值的超分辨率图像重建[J]. 中国光学, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532
HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J]. Chinese Optics, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532
Citation: HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J]. Chinese Optics, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532

稀疏阈值的超分辨率图像重建

doi: 10.3788/CO.20160905.0532
基金项目: 

应用光学国家重点实验室自主基金资助项目 Y4223FQ141

详细信息
    作者简介:

    何阳(1990-), 男, 湖南常德人, 硕士研究生, 2013年于华中科技大学获得学士学位, 主要从事超分辨图像重建方面的研究.E-mail:merelyyang@163.com

    通讯作者:

    黄玮(1965-), 男, 吉林长春人, 研究员, 博士生导师, 主要从事光学系统设计方面的研究.E-mail:huangw@ciomp.ac.cn

  • 中图分类号: TP394.1;TH691.9

Super-resolution image reconstruction based on sparse threshold

Funds: 

Foundation Project of State Key Laboratory of Applied Optics of China Y4223FQ141

More Information
  • 摘要: 为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1dB,平均结构自相似度(SSIM)为0.9379,平均计算时间为10.2s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。
  • 图  1  3倍超分辨率Lena图像重建结果

    Figure  1.  Results of 3×super-resolultion reconstruction for image of Lena

    图  2  3倍超分辨Pepper局部重建结果

    Figure  2.  Local details of 3×super resolultion reconstruction for iamge of Pepper

    表  1  3种算法重构图像峰值信噪比(PSNR/dB)和结构自相似度(SSIM)对比

    Table  1.   Comparison of PSNRs and SSIMs by three methods

    标准图像Bicubic算法Yang算法本文算法
    Barbara26.2/0.880 126.4/0.887 026.7/0.901 5
    Bridge24.4/0.870 524.8/0.899 924.9/0.903 0
    Foreman31.2/0.908 732.0/0.915 333.4/0.932 7
    Lena31.7/0.954 932.6/0.957 732.9/0.968 2
    Pepper32.4/0.969 933.3/0.965 134.3/0.979 1
    Zebra26.6/0.914 928.0/0.935 828.6/0.943 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-05-11
  • 修回日期:  2016-06-13
  • 刊出日期:  2016-10-01

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