Polarization Image Super-resolution with Non-local Sparse Models

The different polarization phase angle (orientation) low-resolution images of the same scene have much redundant and complementary information which can be used to construct a high-resolution image. In this paper, we propose a super-resolution (SR) algorithm via sparse and redundant representation with considering the non-local self-similarity in different polarization orientation images. As the redundant over-complete dictionary has many irrelevant atoms which not only reduce the computational efficiency in sparse coding but also reduce the representation accuracy, we learn a local dictionary by applying the principal component analysis (PCA) technique. For an image patch to be coded, the best fitted sub-dictionary is adaptively selected by an adaptive sparse domain selection strategy. To improve the stability and accuracy of sparse coding, the centralized sparse coding algorithm is used. The extensive experimental results demonstrated that the proposed method can effectively reconstruct the polarization image with edge structure preserved and detailed information obtained in terms of PSNR, SSIM and visual perception.