基于自适应改进的压缩域红外弱小目标检测

现有压缩域目标检测算法取得较好检测结果的同时，有效减少了数据存储空间，但是存在背景参数估计易受噪声影响，目标检测易对邻近目标产生漏警等问题。在原有压缩域红外小目标检测算法的基础上进行改进，提出了一种基于自适应参数估计和噪声统计模型的压缩域目标检测算法。对压缩域红外数据矩阵进行自适应的低秩稀疏分解，分离并重建背景矩阵和目标矩阵，根据分解残差推导统计模型，对目标矩阵进行基于噪声统计模型的阈值分割。结果表明，此算法较原算法具有更好的抗干扰能力，并解决了邻近目标的漏警问题。

Abstract

The existing infrared target detection algorithm in compressive domain achieves obtain good performance with low required data storage, but have its own shortcomings. One shortcoming is the difficulty to estimate background parameters, which are sensitive to noise and complex background, the other is the high false dismissal probability when targets are close to their neighbors. Considering those shortcomings, an infrared small target detection algorithm in compressive domain based on self- adaptive parameter configuration and noise statistics is proposed. The original infrared image is projected on a sensing matrix to obtain the measurement vector. The sparse target matrix and the low-rank background matrix can be recovered and separated simultaneously from the measurements based on low- rank and sparse matrix decomposition in compressive domain with adaptive parameter. The infrared small target detection is realized by threshold segmentation of statistical model of noise. Results indicate that the proposed method outperforms the previous method in both subjective and objective qualities under complex infrared background with less data storage, and solves the false dismissal probability problem when targets are close to their neighbors.

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李安冬, 林再平, 安玮, 杨林娜. 基于自适应改进的压缩域红外弱小目标检测[J]. 中国激光, 2015, 42(10): 1008003. Li Andong, Lin Zaiping, An Wei, Yang Linna. Infrared Small Target Detection in Compressive Domain Based on Self-Adaptive Parameter Configuration[J]. Chinese Journal of Lasers, 2015, 42(10): 1008003.

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