Dim and Small Target Detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

To improve the detection ability of infrared dim and small targets in complex cloud backgrounds, we present a novel method based on motion energy estimation. First, according to the characteristic of the gradient between the target and the background, we propose a new diffusion function and combine with the gradient difference to complete the background suppression. Then an improved high-order correlation algorithm is proposed to enhance the target signal through the motion correlation properties of the target, which build the gradient difference discrimination model between the target and four adjacent directions to establish the region of interest model, then realizes the high-order energy enhancement of the region of interest and further eliminate the clutters at the same time. Finally, the motion model of the target in the inter-frame spatial-temporal domain is constructed to estimate the motion direction of the target, then realizes the target energy accumulation along the motion direction and obtains the maximum energy of the target to detect the real target. In the experiment, we select six scenes for testing, the experimental results show that the proposed method has an excellent detection performance compared with six traditional detection algorithms in infrared dim and small target detection.