针对自然图像的模糊强度检测和局部模糊区域分割,提出一种无参考无训练的检测分割算法.首先对待测图像进行再模糊;然后对再模糊图像和待测图像逐点进行小邻域离散余弦变换,得到待测图像的模糊强度分布;最后结合K-Means聚类算法和形态学运算对图像的局部模糊区域进行分割提取.实验结果表明,采用文中算法得到的模糊强度分布图能够有效地检测和分割图像的清晰区域与模糊区域;与同类算法相比,对于不同模糊形式和不同复杂度的图像,该算法在查准率、查全率和F值等图像分割性能指标上表现较为优异,与人眼主观分割结果具有较高一致性,且该算法无需进行数据训练,具有较高的时间效率.%A no-reference and training-free algorithm was proposed to investigate the detection and segmentation of partial blur for natural images. First, the test image was re-blurred by a Gaussian low-pass filter. Then pix-el-wise discrete cosine transformations within micro neighborhoods for both the test image and the re-blurred image were conducted to obtain the blurriness distribution map. Finally, combined with theK-Means clustering algorithm and the morphologic closing operation, the test image could be segmented into blur region and non-blur region. A series of natural images containing out-of-focus, object motion and different complexity were examined. Experiment results demonstrate that the proposed approach can effectively detect and segment the partial blur image and behave well at precision, recall and F-score. Moreover, the proposed approach has a relatively strong consistency with human judgment and a relatively high time efficiency due to no data training.
展开▼
