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Polarimetric imaging can significantly improve the quality of images captured under poor imaging conditions, these include enhancing visibility in fog, increasing image contrast under complicated background, highlighting details of targets by removing strong surface glare, etc. Compared with other polarimetric imaging systems, the full-Stokes-vector division-of-aperture polarimetric camera has obvious advantages such as the real-time imaging and the structural compactness, owing to its inherent capability of capturing the same scene's four images related to its full-Stokes vector simultaneously. However, the deviations among four polarized images are inevitable due to assembly errors of four subapertures and the corresponding imaging channels. In this paper, we propose a method to solve the image registration problem in such a kind camera. Firstly, the coarse image registration is implemented through the phase-only correlation algorithm; secondly, the feature points of the reference image and other three images under registration are extracted and matched by using the Speeded-up Robust Features (SURF) algorithm; finally, the affine transformation matrix between the reference image and each of the three images to be registered is obtained by using the RANSAC (i.e., Random Sample Consensus) optimization algorithm, respectively. The experimental results demonstrate that four polarized images are registered, which effectively enhance the image quality in terms of the degree-of-polarization (DoP), the angle-of-polarization (AoP), the structural similarity (SSIM) index, and the normalized mutual information (NMI) index.

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