基于蚁群优化算法的弱光图像显著性目标检测

近年来，显著性目标检测受到工业界和学术界的大量关注，成为了计算机视觉领域中一项重要的基础研究，该问题的解决有助于各类视觉任务取得突破性进展。尽管针对可见光场景的显著性检测工作已经取得了有效成果，但如何在信噪比偏低、可用有效信息匮乏的弱光图像中提取边界清晰、内部结构准确的显著性目标，仍然是具有挑战性的难题。针对弱光场景下显著性目标检测存在边界模糊、结构不完整等造成准确率较低的问题，提出基于蚁群优化（ACO）算法的显著性检测模型。首先，通过多尺度超像素分割将输入图像转换为具有不同节点的无向图；其次，基于最优特征选择策略来更充分地获取低对比度弱光图像中所包含的更多显著目标的特征信息，并摒弃冗余的噪声信息；然后，引入空间对比度策略用于探索弱光图像中具有相对较高对比度的全局显著性线索。而为了在低信噪比情况下也能获取准确的显著性估计，利用ACO算法对显著图进行优化。通过在3个公共数据集（MSRA、CSSD和PASCAL-S）以及夜间弱光图像（NI）数据集上进行实验，可以看出，所提模型在3个公共数据集上的曲线下面积（AUC）值分别达到了87.47%、84.27%和81.58%，在NI数据集上的AUC值比排名第2的低秩矩阵恢复（LR）模型提高了2.17个百分点。实验结果表明，相较于11种主流的显著性检测模型，所提模型具有结构更准确且边界更清晰的检测效果，有效抑制了弱光场景对显著性目标检测性能的干扰。

Salient object detection in weak light images based on ant colony optimization algorithm

With substantial attention being received from industry and academia over last decade, salient object detection has become an important fundamental research in computer vision. The solution of salient object detection will be helpful to make breakthroughs in various visual tasks. Although various works have achieved remarkable success for saliency detection tasks in visible light scenes, there still remain a challenging issue on how to extract salient objects with clear boundary and accurate internal structure in weak light images with low signal-to-noise ratios and limited effective information. For that fuzzy boundary and incomplete internal structure cause low accuracy of salient object detection in weak light scenes, an Ant Colony Optimization (ACO) algorithm based saliency detection framework was proposed. Firstly, the input image was transformed into an undirected graph with different nodes by multi-scale superpixel segmentation. Secondly, the optimal feature selection strategy was adopted to capture the useful information contained in the salient object and eliminate the redundant noise information from weak light image with low contrast. Then, the spatial contrast strategy was introduced to explore the global saliency cues with relatively high contrast in the weak light image. To acquire more accurate saliency estimation at low signal-to-noise ratio, the ACO algorithm was used to optimize the saliency map. Through the experiments on three public datasets (MSRA, CSSD and PASCAL-S) and the Nighttime Image (NI) dataset, it can be seen that the Area Under the Curve (AUC) value of the proposed model reached 87.47%, 84.27% and 81.58% on three public datasets respectively, and the AUC value of the model was increased by 2.17 percentage points compared to that of the Low Rank Matrix Recovery (LR) model (which ranked the second) on the NI dataset. The results demonstrate that the proposed model has the detection effect with more accurate structure and clearer boundary compared to 11 mainstream saliency detection models and effectively suppresses the interference of weak light scenes on the detection performance of salient objects.