Abstract:

A surface target enhancement method based on the virtual variational polarization theory is proposed, which makes full use of the inherent characteristics of infrared polarization information. By using the description relationship between the stokes vector of the incident light and the intensity of the emitted light, the analytical relationship between the intensity of the emitted light and the polarizing angle is derivated, and thus virtually realizes the intensity of the emitted light with any polarizing angle; then according to the surface target criterion of the maximum signaltonoise ratio, the searching of optimal polarizing angle is iteratively realized by using the particle swarm algorithm, and finally get the enhanced infrared surface target image. The feasibility and validity of the algorithm are validated by using the real long wave infrared polarization image. Experimental results show that, the enhanced image using the proposed algorithm has better quality under three image quality evaluation indexes of gray scale contrast, average grads, and image entropy, which lays a good foundation for realizing the following algorithms of target detail recognition and attacking point choosing.