相入喜, 许清泉, 朱锡芳, 吴峰, 汤毅. 基于局部锐度特征的无参模糊图像质量评估算法研究[J]. 微电子学与计算机, 2017, 34(3): 125-128, 132.
引用本文: 相入喜, 许清泉, 朱锡芳, 吴峰, 汤毅. 基于局部锐度特征的无参模糊图像质量评估算法研究[J]. 微电子学与计算机, 2017, 34(3): 125-128, 132.
XAING Ru-xi, XU Qing-quan, ZHU Xi-fang, WU Feng, TANG Yi. No-reference Blur Image Assessment Based on Local Sharpness[J]. Microelectronics & Computer, 2017, 34(3): 125-128, 132.
Citation: XAING Ru-xi, XU Qing-quan, ZHU Xi-fang, WU Feng, TANG Yi. No-reference Blur Image Assessment Based on Local Sharpness[J]. Microelectronics & Computer, 2017, 34(3): 125-128, 132.

基于局部锐度特征的无参模糊图像质量评估算法研究

No-reference Blur Image Assessment Based on Local Sharpness

  • 摘要: 为了有效评估模糊图像的质量, 在图像局部锐度特征的基础上, 提出一种基于局部锐度特征和双树复小波相结合的无参图像的质量评估算法.该算法首先对评估图像进行多层双树复小波分解, 进而重构6个不同方向的图像, 接着计算每个方向图的局部锐度特征, 最后通过加权线性融合得到评估图像的锐度值.通过在4个公共的图像评估数据集验证, 结果表明所提出评估方法在模糊图像评估中优于其他6种传统的图像质量评估方法, 同时也证明了评估结果更接近人的主观视觉特性.

     

    Abstract: In order to effectively assess the quality of the image, the paper proposes a novel assessment method without the reference image that effectively combines the dual tree complex wavelet transform with the local sharpness of the image on the basis of the local sharpness of the image. Firstly, the image is decomposed multilayers by the dual tree complex wavelet and 6 different directions of the image are reconstructed, then the local sharpness of each direction is computed. Finally, the final local sharpness is computed by the linearly fusion with the weight. In 4 publicly available image databases, experimental results show that the proposed method is superior to other 6 kinds of the state-of-the-art methods in the blur images from the above databases and testify that the proposed method is closer to the human vision.

     

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