Infrared small-target detection method based on mixing Gauss and sparse representation

Abstract

The invention provides an infrared small-target detection method based on mixing Gauss and sparse representation. The method comprises the following steps: constructing an overcomplete morphological dictionary of an image in a self-adaptive mode by adopting a K cluster singular value decomposition K_SVD method; according to the characteristic that object signals are usually distributed in a Gauss mode, dividing the atoms of the self-adaptive overcomplete morphological dictionary into target atoms representing target forms and background atoms representing background noise components by using a Gauss overcomplete dictionary, and forming a self-adaptive mixing Gauss overcomplete dictionary having a target morphological dictionary and a background morphological dictionary; performing sparse representation on an original image block in the mixing Gauss overcomplete dictionary and extracting the sparse representation coefficient of an image signal; and when the rarefication degree represented by the sparse representation coefficient is greater than a threshold, determining that the image block contains a target, otherwise determining that the image block is a background. By using the method provided by the invention, defects can be overcome that it is difficult for a conventional Gauss sparse dictionary to be adaptive to non-Gaussian distributed target forms and judging whether a target is contained by Gauss atom sparse representation coefficients, and thus the detection performance of small and weak targets can be improved.

Description

technical field [0001] The invention belongs to the field of measurement and control of deep-space aircraft, and specifically relates to the detection of infrared weak and small moving targets. It is a core technology of infrared imaging search and tracking systems, target monitoring systems, satellite remote sensing systems, and safety inspection systems. The system can have a wide range of applications. Background technique [0002] In various imaging detection and tracking systems, it is required to be able to intercept and lock the tracking target as soon as possible. When the distance between the detector and the target is long, the target appears as a small target with only a few pixels in imaging, and is easy to be submerged in various clutter backgrounds and strong noise, which brings great difficulties to target detection and tracking. It is very difficult. [0003] At present, the infrared small target detection algorithm based on a single frame can be divided in...

AI Technical Summary

Problems solved by technology

Gaussian samples and sparse dictionaries with Gaussian models are suitable for small and weak targets with Gaussian distribution, and the shape of small and weak targets changes dynamically. It is difficult for Gaussian models to adapt to unstructured forms such as non-Gaussian distributions. The adaptability and detection capabilities need to be further strengthened

Images