Optimizing image registration by mutually exclusive scale components

Local optimum has been one of the most difficult problems in image registration notwithstanding the extensive research effort that has been put into solving it. Local optimums occur when a portion of patterns in the floating image coincide with a portion of patterns in the reference image even though the two images are not entirely marched. Existing hierarchical or multi-scale methods suffer from this problem mainly because some redundant information that causes local optimums appears in multiple scales. We propose to avoid it by decomposing an image into several mutually exclusive scale components so that minimal redundant information is present. Our method is evaluated and compared with existing methods using high resolution satellite imagery where thousands of local optimum traps are hidden. We show that our method has significant improvement over existing solutions in both robustness and efficiency.