SCoBeP: Dense image registration using sparse coding and belief propagation

Image registration as a basic task in image processing has been studied widely in the literature. It is an important preprocessing step in various applications such as medical imaging, super resolution, and remote sensing. In this paper, we proposed a novel dense registration method based on sparse coding and belief propagation. We used image blocks as features, and then we employed sparse coding to find a set of candidate points. To select optimum matches, belief propagation was subsequently applied on these candidate points. Experimental results show that the proposed approach is able to robustly register scenes and is competitive as compared to high accuracy optical flow Brox et al. (2004) [1], and SIFT flow Liu et al. [2].

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► SCoBeP is a novel dense image registration method.
► The proposed method relies on sparse coding and belief propagation.
► SCoBeP provides decent results in both wide-baseline and short-baseline images.
► SCoBeP is competitive comparing to SIFT flow and optical flow.