College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2020 Jan 14;20(2):459. doi: 10.3390/s20020459.
Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.
针对天空背景下不同目标尺寸、低信噪比的红外多目标图像中目标检测不完整、边界不清晰的问题，提出了一种基于多显著度检测的多目标显著度检测方法。该方法主要对红外图像中的多个目标区域进行增强处理，背景区域进行抑制处理。首先对图像进行多尺度顶帽变换（Top-hat），通过腐蚀和膨胀操作提取图像的亮暗区域相减部分，并对图像进行重构，减少天空模糊背景噪声的干扰。然后将多尺度顶帽变换后的图像从时域转换到频域，直接提取频谱残差和相位谱，通过多尺度高斯滤波重建得到两种图像显著度图。另一方面，直接提取相位谱的四元数特征，然后对相位谱进行重构，通过高斯滤波得到一种图像显著度图。最后将上述三种显著度图进行融合，完成红外图像的显著度检测。实验结果表明，通过对红外视频图像的实验分析以及对接收者操作特性（ROC）曲线和曲线下面积（AUC）指标的对比分析，该方法生成的红外图像显著度图目标细节清晰，背景抑制效果良好，AUC 指标性能优良，达到 99%以上。有效提高了天空背景下红外图像的多目标显著度检测效果，有利于后续对图像目标的检测和跟踪。
