为了解决局域对比度方法(LCM)无法检测局部亮区损伤目标和分离效率低的问题, 本文提出了基于邻域向量内积局部对比度图像增强的光学元件损伤目标检测方法。首先, 将图像中的每一个点的3×3邻域生成一个9维邻域向量, 并将邻域内的最大值扩展成一个9维度极值向量, 计算邻域向量与极值向量的内积; 其次, 计算每个像素的邻域向量内积对比度值(NVDC); 然后, 计算每个像素的邻域向量内积局部对比度, 即在一个较大的区域内(5×5)搜索当前像素所有邻域向量内积对比度的最大值, 作为当前像素的邻域向量内积局部对比度值(NVDLC); 最后, 对NVDLC图像进行二值化和目标分离。实验结果表明, 通过本文的增强方法使得损伤图像的信噪比从3.775提高到12.445, 损伤目标信号得到极大的增强。在经过邻域向量内积局部对比度方法图像增强后, 能够直接使用自适应阈值公式将小于2 pixel的损伤目标从背景中分离出来, 满足了弱对比度损伤目标检测对于精度和效率的要求。

To solve the problems of the inability of LCM in detecting damaged targets in local bright areas and its low separation efficiency, a damage detection method based on local contrast image enhancement of a neighborhood vector local dot was proposed in this study. First, a nine-dimensional neighborhood vector was generated from the 3×3 neighborhood of each point in the image, and the maximum value in the neighborhood was extended to the nine-dimensional extreme vector in calculating the dots of the neighborhood vector and extreme vector. Second, the Neighborhood Vector Dot Contrast (NVDC) of each pixel was calculated. Third, the NVDC of the image patch of each pixel was calculated,i.e., the maximum NVDC value of each pixel in a relatively large area(5×5) was taken as the final Neighborhood Vector Dot Local Contrast (NVDLC) of the corresponding pixel. Fourth, the NVDLC image was binarized, and the targets were separated from the background. The experimental results demonstrate that the LSNR of the damaged image is improved from 3.775 to 12.445, and the signal of the damage target is significantly enhanced using the image enhancement method proposed in this study. After the maximum contrast image of NVDLC is enhanced, the damage targets with size of less than 2 pixels can be directly separated from the background using the adaptive threshold formula, which meets the accuracy and efficiency requirements of the weak contrast damage target detection.