Dim Target Detection Based on Optical Flow Histgram in Low Frame Frequence in Clouds Background

Fusion Detection Algorithm for Infrared Dim Target based on Temporal-Spatial Domain Accumulation and Difference

“…In this paper, the bigger size template 1 H is used for image filtering, it will be good estimation to the background image, and the small size smoothing template 2 H is used to suppress part background noises for image filtering, the difference between 1 H filtering and 2 H filtering is the results of spatial difference. The filtering templates used in this paper are as follows: denotes the processing result of small scale template 2 H filtering.…”

“…The filtering templates used in this paper are as follows: denotes the processing result of small scale template 2 H filtering. The target is enhanced and some isolated noise points also exist after the filtering template 1 H , The target signal is suppressed after the filtering template 2 H , the background information is mainly obtained. Spatial difference processing suppresses the correlation of the background in spatial domain, and the target is enhanced.…”

“…At present, the low signal-to-noise ratio (SNR) infrared dim target detection is one of the core difficult problems of infrared search and track (IRST) systems, it always is the research hot spot and difficulty in this field [1][2]. As for IRST systems, the target is demand to possible earliest detect at a long distance (typically a few tens of kilometers or even hundreds of kilometers), since the influence of many factors such as complex battle environment, inhomogeneous atmospheric thermal radiation, internal noise of the detector, the target only about one or a few pixels in infrared images, the infrared radiation intensity is weak, so the SNR of the target is low, the target almost is submerged in clutter background, and sometimes even is lost, all these bring about great difficulties to dim target detection.…”