Blind image sharpness assessment based on local contrast map statistics
Blind image sharpness assessment based on local contrast map statistics
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DOI：
10.1016/j.jvcir.2017.11.017
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发表时间：
2018-01-01
影响因子：
2.6
通讯作者：
Grgic, Mislav
中科院分区：
文献类型：
--
作者：
Gvozden, Goran;Grgic, Sonja;Grgic, Mislav
本文提出了一种快速盲图像清晰度/模糊度评估模型（BISHARP），它在空间域和变换域操作。该模型通过计算局部邻域内每个图像像素的均方根值来生成局部对比度图像图。然后将得到的局部对比度图变换到小波域中，其中在存在变化的模糊强度的情况下评估高频内容的减少。据发现，百分位值计算排序，电平移位，高频小波系数可以作为可靠的图像清晰度/模糊度估计。此外，发现对比度图的更高动态范围显著提高了模型性能。在七个图像数据库上进行的验证结果显示出与感知分数非常高的相关性。由于低的计算要求，该模型可以很容易地利用在真实的世界的图像处理应用。
This paper presents a fast blind image sharpness/blurriness assessment model (BISHARP) which operates in spatial and transform domain. The proposed model generates local contrast image maps by computing the root mean-squared values for each image pixel within a defined size of local neighborhood. The resulting local contrast maps are then transformed into the wavelet domain where the reduction of high frequency content is evaluated in the presence of varying blur strengths. It was found that percentile values computed from sorted, level-shifted, high-frequency wavelet coefficients can serve as reliable image sharpness/blurriness estimators. Furthermore, it was found that higher dynamic range of contrast maps significantly improves model performance. The results of validation performed on seven image databases showed a very high correlation with perceptual scores. Due to low computational requirements the proposed model can be easily utilized in real world image processing applications.