A cascade method for infrared dim target detection

Infrared dim target detection acts a pivotal role in searching and tracking applications. Due to the long-range distance, detection tasks are usually confronted with a low signal-to-noise ratio (SNR). To tackle the problem, a cascade method is proposed in this paper. Different from traditional gradient detection, the proposed method takes advantage of movement continuity to settle the pseudo targets elimination. Concisely, there are only three steps contained in cascade method. Initially, since some bad points might exist in the original infrared images, a local order-statistic filter is adopted to dispel pixel-sized fixed noise (PSFN). Secondly, processed infrared images are filtered by a 5 x 5 facet kernel to extract candidate targets from background. Finally, inspired by the movement continuity, a motion continuous pipeline (MCP) is built to achieve pseudo objects elimination. To measure the performance of the proposed method, a set of real infrared images (the SNR is around 1) covering different backgrounds are discussed in experiments. The results indicate that the proposed method outperforms conventional baseline methods in terms of detection rate and false-alarm.