基于非线性抗噪声估计的视觉显著性弱小目标检测

针对红外图像处理技术中弱小目标检测的重要性及关键性，提出一种基于非线性抗噪声估计的检测算法来解决高可靠性、高鲁棒性的弱小目标检测问题。提出的方法基于传统视觉显著度算法及空间距离处理方法，对目标及背景区域采用非线性加权方法进行估计，在不显著降低目标信号信噪比的基础上，削弱孤立微小噪声点对检测算法性能的影响，可提高抗噪性能。首先，采用模块化及非线性映射方式预测背景；然后，融入距离相关因子滤除噪声干扰；最后，在处理结束的图像上进行二值化阈值分割，自动检测并向下一级处理软件输出目标位置信息。实验结果表明:提出的算法与近年来先进的弱小目标检测算法相比，在受试者测试曲线上，在相同的虚警率下，可获得更高的检测率，对背景噪声的抑制很明显；在局部信噪比及背景抑制因子的测试比对数据上，提出的算法可获得更高的检测指标。缺点是算法采用了非线性处理技术，运算效率较低，需进一步优化算法以提高计算速度，实现算法的实时目标检测。

Visual salient dim small target detection based on nonlinear anti noise estimation

Aiming at the importance and key of dim small target detection in infrared image processing technology, a detection algorithm based on nonlinear anti noise estimation is proposed to solve the problem of dim small target detection with high reliability and robustness. Based on the traditional visual saliency algorithm and spatial distance processing method, the proposed method uses the nonlinear weighting method to estimate the target and background area. On the basis of not significantly reducing the signal-to-noise ratio of the target signal, the influence of isolated small noise points on the performance of the detection algorithm can be weakened, and the anti-noise performance can be improved. Firstly, the background is predicted by modular and nonlinear mapping, and then the distance correlation factor is integrated to filter out the noise interference. Finally, the binary threshold segmentation is carried out on the processed image to automatically detect and output the target position information to the next level processing software. The experimental results show that the proposed algorithm can obtain a higher detection rate on the subject test curve under the same false alarm rate and significantly suppress the background noise compared with the advanced weak and small target detection algorithm in recent years; On the test comparison data of local signal-to-noise ratio and background suppression factor, the proposed algorithm can obtain higher detection indexes. The disadvantage is that the algorithm adopts nonlinear processing technology and has low operation efficiency. It needs to further optimize the algorithm to improve the calculation speed and realize the real-time target detection of the algorithm.