Polarization detection of space targets is one of the most important research
directions in the field of space target recognition. In view of the
fact that there are problems such as strong background noise and
inconspicuous details of contour features in the polarization image of
space targets, an image denoising and enhancement strategy is
proposed. To solve the problem of high intensity of Gaussian noise in
degree of polarization (DoP) images, a denoising method named adaptive
noise template prediction (ANTP) is proposed to eliminate the noise.
Compared to the existing methods, the ANTP algorithm performs better
at reducing noise and improving image quality. Aiming at the
difficulty of separating the background noise from angle of
polarization (AoP) images, a denoising method named gray analysis of
local area (GALA) is proposed. Compared to traditional methods, the
GALA algorithm can effectively extract the contour features of targets
and improve the contrast of AoP images. An image fusion method based
on discrete cosine transform and local spatial frequency (LSF) is used
to fuse the denoised DoP image and AoP image. The experimental results
of the simulated and real space target polarization detection confirm
the effectiveness of our proposed strategy.