No-reference Blur Image Assessment Based on Local Sharpness

XAING Ru-xi; XU Qing-quan; ZHU Xi-fang; WU Feng; TANG Yi

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No-reference Blur Image Assessment Based on Local Sharpness

更新时间：2026-09-04

- No-reference Blur Image Assessment Based on Local Sharpness
- Vol. 34, Issue 3, Pages: 125-128(2017)
- 作者机构：
  
  1. 常州工学院机构电气与光电工程学院,江苏,常州,213022
  2. 常州光电子材料重点实验室,江苏,常州,213022
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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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, et al. No-reference Blur Image Assessment Based on Local Sharpness[J]. 2017, 34(3): 125-128.
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. DOI：

XAING Ru-xi, XU Qing-quan, ZHU Xi-fang, et al. No-reference Blur Image Assessment Based on Local Sharpness[J]. 2017, 34(3): 125-128. DOI：

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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references

Liu Lixiong, Liu Bao, Huang Hua, et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing: Image Communication, 2014, 29(8): 856-863. DOI: 856

Kingsbury N G. Complex wavelets for shift invariant analysis and filtering of signals[J]. Applied and Computational Harmonic Analysis, 2001, 10(3) : 234-253. DOI: 234

Rania Hassen, Zhou Wang, Magdy M A Salama. Image sharpness assessment based on local phase coherence[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2798: 2810. DOI: 2798

Xue W, Mou X, Zhang L, et al. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4850-62. DOI: 4850

Narvekar N, Karam L. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)[J]. IEEE Transactions on Image Processing, 2011, 20(9): 2678-2683. DOI: 2678

Vu C, Phan T, Chandler D. S3: A spectral and spatial measure of local perceived sharpness in natural images[J]. IEEE Transactions on Image Processing, 2012, 21(3): 934-945. DOI: 934

Mittal A, Moorthy A, Bovik. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708. DOI: 4695

桑庆兵, 梁狄林, 吴小俊, 等. 基于支持向量回归的无参考模糊和噪声图像质量评价方法[J]. 光电子·激光, 2014, 24(3): 595-601. DOI: 595

韩沁, 李凡, 汪烈军. 基于互相关系数的边缘加权质量预测评估算法[J]. 微电子学与计算机, 2014, 31(10): 107-112. DOI: 107

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