奇异值分解域差异性度量的低景深图像显著性目标提取方法

针对低景深图像(DOF)目标提取过程中，容易出现目标提取不完整或背景被误识为目标等现象，该文提出一种奇异值分解 (SVD)域差异性度量的低景深图像目标提取方法。先对低景深图像进行高斯模糊，以图像中每一个像素点为中心，利用滑动窗口分别截取模糊前后图像上相同位置的图像块并进行奇异值分解，再构造两奇异值之间的差异特征向量，针对此向量定义低中高全频段信息加权的差异性度量算子，计算对应像素点的显著性特征值，逐像素处理得到显著性结果图并进行阈值化处理，实现低景深图像目标的有效提取。对大量低景深图像进行处理，并与几种现有方法进行比较，提出方法的F度量值最大可提高54%，平均绝对误差减少76%～87%，可完整提取目标并有效去除背景，具有较强的可靠性。

Salient Target Extraction from Low Depth of Field Images Based on Diversity Measure in Singular Value Decomposition Domain

In the process of target extraction in low Depth Of Field (DOF) image, it is easy to get incomplete target extraction or the background is mistakenly recognized as a target. A low DOF image target extraction method based on Singular Value Difference (SVD) measurement is proposed. Firstly, Gaussian blur is applied to the low DOF image. Taking the current pixel as the center, the image blocks at the same position on the image before and after blur are intercepted by using the sliding window, and singular value decomposition is carried out. Then, the difference feature vector between the two singular values is constructed. Based on this vector, the difference measurement operator is defined to calculate the characteristic intensity value of the corresponding pixel. The feature salient map is obtained by pixel by pixel processing, and the threshold processing is carried out to realize the effective extraction of low DOF image targets. A large number of low DOF images are processed, and compared with several existing methods, the maximum F measure can be increased by 54%, and the average absolute error can be reduced by 76%～87%. The proposed method can completely extract the target and effectively remove the background, and has strong reliability.