Dual deblurring leveraged by image matching

Existing dual image deblurring methods usually model blurred image pairs being taken from exactly the same viewpoint and restore a single clear image. This imposes a strong assumption that the latent clear images of both images must be completely identical. In contrast to this restricted scenario, we assume that the restored pair are different, but can be approximated by image warping due to small viewpoint change. This allows us to deblur each image individually, but still being able to make use of the matched areas in image pairs. Our deblurring algorithm iteratively performs a two-directional dual image deblurring, which uses the Split Bregman method, and matches the latent clear image pairs by a homography. Experiments show that the proposed algorithm automatically recovers clear images from blurred image pairs in the same scene. Statistics suggest that the method is robust to viewpoint change and different noise levels.