基于模糊检测概率变化的模糊图像质量评价算法

为了解决无参考模糊图像质量评价中缺少人眼视觉特性的问题,提出了一种基于模糊检测概率变化的模糊图像质量评价算法,该算法首先对图像进行预处理,利用改进的自适应算法计算模糊图像的特定显著阈值,并通过显著阈值对图像进行二值化,得到图像的最终显著区域。然后,通过再模糊后两幅图像显著区域的模糊检测概率的变化情况描述图像质量。变化越大,表示图像质量越清晰。实验结果表明,该算法在LIVE数据集中取得了较好的实验效果,并与现有算法相比,具有更好的评价性能。同时,该算法也可以用于"智能煤矿"等领域。

Blur Image Quality Assessment Method Based on Blur Detection Probability Variation

To solve the problem of lack of human visual characteristics in the non-reference blur image quality assessment.This paper proposes a blur image quality assessment method based on blur detection probability variation.This algorithm firstly preprocesses the image,uses the improved adaptive method to calculate the specific salient threshold of blurred image and binarizes the image with a specific threshold to obtain the final salient region of the image.Then,the image quality is described by the blur detection probability variation of the salient regions of the two images after re-blurring.The larger the change,the clearer the image quality.Experimental results show that the proposed algorithm achieves better experimental results in the LIVE data set and has better evaluation performance than the existing traditional algorithms.At the same time,the proposed algorithm can also be used in the field of wisdom mine and so on.