基于联合方向梯度和均值对比度的红外弱小目标检测方法

针对小目标在整幅图像中占比很低，且目标周围存在大量杂波，提出了一种基于联合方向梯度（Associated Directional Gradient，ADG）和均值对比度（Mean Contrast，MC）的红外弱小目标检测算法。该算法由两个模块组成：ADG利用红外弱小目标的高斯分布模型，将单一方向的梯度与一个相邻方向上的梯度相加组成新的联合梯度特征，增强真实目标、抑制背景杂波的同时能够消除高亮边缘对目标检测效果的影响；MC融入方向信息来计算目标的多方向对比度，选用多方向对比度的最小值抑制结构噪声，并将均值滤波的思想引入对比度的计算，抑制背景中的孤立噪声，进一步降低检测的虚警率。在实际红外图像上的实验结果表明，该算法在增强目标信噪比和抑制背景噪声方面，能够取得较好效果。

Infrared small target detection based on associated directional gradient and mean contrast

The detection of infrared small targets has been a challenging task in the field of computer vision due to the low percentage of small targets in the whole image and the presence of a large amount of clutter around the targets. We propose an algorithm based on associated directional gradient and mean contrast. The algorithm consists of two modules： the associated directional gradient module uses a Gaussian distribution model of infrared small targets， and adds the gradient in a single direction with the gradient in an adjacent direction to form a new feature called associated directional gradient， which enhances the real target， suppresses background clutter， and eliminates the effect of highlighting edges on the target detection. The mean contrast module incorporates directional information to calculate multi-directional contrast of the target. The minimum value of multi-directional contrast is chosen to suppress structural noise， and the idea of mean filtering is introduced into the calculation of contrast to suppress isolated noise in the background and further reduce the false alarm rate of detection. Experimental results on actual infrared images show that the algorithm can achieve better results in enhancing the signal-to-noise ratio of the target and suppressing the background noise.