Abstract
This paper presents an algorithm to calculate polarized images, based on spatially adaptive wavelet analysis, in which image fusion theory is used. According to the principle and method of polarization imaging, the shortcomings of traditional methods in preserving detail information, removing the noise, and dealing with the misalignment of components in polarimetry are analyzed. Polarized-image calculation is a special case of image fusion, in which the combination rule is fixed. At the same time, wavelet-based image fusion method has a special advantage in acquiring rich detail information. To remove the effects of noise, we propose a spatially adaptive wavelet transform method. Then this method is extended to translation-invariant wavelets, which yield better results than the orthogonal wavelet transform when there is misalignment among components in polarimetry. Experiment and simulation results show that spatially adaptive wavelet-based polarization imaging yields significantly superior image quality to the traditional method.
