大气湍流下退化序列图像的目标检测方法

为解决大气湍流退化序列中运动目标检测困难的问题，提出了一种结合低秩分解和检测融合的目标检测方法。首先，根据退化视频中湍流运动分量的稀疏分布特点，采用低秩矩阵描述法将每帧图像分解为低秩稳像和稀疏运动两部分，初步实现场景和湍流运动的粗分离。其次，由于稀疏部分中包含目标在内的整个场景的稀疏运动量，引入自适应阈值法剔除干扰量，分割目标并填补其中空洞；对于无湍流偏移干扰的低秩部分，采用高斯建模获得低秩中的前景区域。最后，对两部分检测结果进行联合判定，从而获得准确的目标检测结果。实验表明，本文方法目标提取的准确度较高，明显优于当前经典检测方法，在强湍流条件下检测结果仍较为理想。

Object Detection in Degraded Images Under Atmospheric Turbulence

Atmospheric turbulence can cause the image degraded with time-varying blur and geometric distortion. We resolve the object detection problem by proposing a three-step approach. According to the low-rank decomposition, the first step decomposes the turbulence sequence into two components:the low-rank stabilized background and the sparse errors. The sparse part in the result of first step includes turbulence distortion, noise and moving object. Then, the sparse matrix is processed with adaptive threshold to select the block-sparse mask and the holes within the mask are simultaneously filled. The low-rank matrix is processed with different Gaussian models to extract the foreground. Finally, a decision fusion module is introduced to exploit complementary information from two approaches to boost overall detection accuracy. The experimental results have shown the promising performances. Compared with traditional methods, the proposed approach can not only improve the detection rate, but also handle the interferences of strong turbulence.