Joint Image Restoration and Matching Based on Hierarchical Sparse Representation

Image matching is widely used in visual-based navigation systems, and most matching methods simply assume the ideal inputs without considering the degradation of real world, such as image blur, which is very common in real-time images. Joint image restoration and matching, such as JRM-DSR is a good way to deal with the degradation of real-time images, which utilizes the sparse representation of the real-time image on the dictionary constructed from the reference image. However, once the size of the reference image is much bigger than that of the real-time image, the size of the dictionary would be so huge that it becomes time-consuming and tough to get the sparse representation. In this paper, we propose a joint image restoration and matching method based on hierarchical sparse representation (JRM-HSR), which shrinks the size of the dictionary with the help of clustering to perform the coarse matching, and then performs the fine matching in a subset of the original dictionary. JRM-HSR is a practical model benefits from the hierarchical structure. In contrast to JRM-DSR, the speed of JRM-HSR is 16 times faster in single sparse representation and 2 times faster in single complete algorithm flow while maintaining the same accuracy.