Image denoising and enhancement strategy based on polarization detection of space targets

Abstract

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.