随着机器视觉和人工智能的快速发展,视觉注意机制作为机器视觉的重要组成部分,受到越来越多的关注。提出一种建立在非下采样轮廓小波变换(NSCT)基础上的从粗到精的显著性检测方法,该方法作为一种基于频域分析的显著性检测算法,能够充分利用图像的低频和高频信息,并能抑制光照对检测造成的影响。模型首先对输入图像进行非下采样轮廓小波分解,对低频分量进行Retinex增强以改善图像亮度的均匀性,从而抑制光照对显著性检测带来的影响,随后对其进行粗糙显著性检测;对高频分量进行非线性增强以抑制噪声并增强细节,重构得到高频特征图,在低频粗糙显著图的范围内对高频特征图进行全局和局部的显著性分析;最后经过融合得到精细显著图。在三个数据集上进行对比实验,验证了所提算法的可行性和有效性。

With the rapid developments of machine vision and artificial intelligence, the visual attention mechanism, as an important part of machine vision, has attracted more and more attention. A coarse-to-fine saliency detection method is proposed based on non-subsampled contourlet transform (NSCT), which, as a frequency-domain based saliency detection method, can make full use of the low-frequency and high-frequency information of images and suppress the influence of illumination on detection as well. First, the non-subsampled contourlet transform is used to decompose the input images. The low-frequency components are enhanced by Retinex to ameliorate the brightness uniformity of images, and thus the influence of illumination on the saliency detection is suppressed. Then, the coarse saliency detection is performed. The high-frequency components are enhanced nonlinearly to suppress noises and enhance details, and thus the high-frequency feature maps are obtained via reconstruction. The global and local saliency analyses of the high-frequency feature maps are performed within the scope of low-frequency coarse saliency maps. Finally, the fine saliency maps are obtained via fusion. The contrast experiments are carried out on three datasets and the results confirm the feasibility and effectiveness of the proposed method.