Joint image restoration and matching method based on distance-weighted sparse representation prior

Abstract

Image matching is a classic problem in the field of image processing, aims to locate the unique position of the real-time images and has been widely used in visual-based navigation systems. However, most of the works on image matching simply assume the ideal inputs without considering the degradation of the real world, such as image blur. In the presence of such a situation, the traditional matching methods usually first resort to image restoration and then perform image matching with the restored image. However, by treating the restoration and matching separately, the accuracy of blurred image matching will be reduced by the defective output of the image restoration. In this paper, we propose a joint blurred image restoration and matching method based on distance-weighted sparse representation (JRM-DSR), which utilizes the sparse representation prior to exploit the correlation between restoration and matching. This prior assumes that the blurred image, if correctly restored, can be well represented as a sparse linear combination of the dictionary constructed by the reference image. In order to achieve more accurate matching results to help image restoration, we consider both local and sparse information as well as adopt distance-weighted sparse representation to obtain better representation coefficients. By iterative restoring the input image in pursuit of the sparest representation, our approach can achieve restoration and match simultaneous, and these two tasks can benefit greatly from each other. Moreover, a coarse-to-fine matching strategy is proposed to further improve the matching accuracy and search efficiency. Experiments demonstrate the effectiveness of our method compared with conventional methods.