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Motion-blurred SIFT invariants based on sampling in image deformation space and univariate search

Scale-invariant feature transform (SIFT) operator is a widely used algorithm to detect local features in images, yet applying this algorithm for motion-blurred image matching is difficult and inefficient. To resolve this issue, this study presents a motion-blurred invariant SIFT algorithm that is based on sample matching in an image deformation reconstructed space. First, the motion-blurred equation is deduced and its controlling parameters to reconstruct the deformation space are discretised. Second, the authors matched samples in the motion-blurred space with the blurred image to identify maximal matching points and optimal parameters. In order to improve the searching efficiency, the authors used a univariate search technique combined with a variable step hill-climbing method to determine the optimal matching. Together, the experimental results show that this improved algorithm has superior matching performance for motion-blurred images.