Joint Image Restoration and Matching Based on Distance-Weighted Sparse Representation

Abstract

Image matching is widely used in visual-based navigation systems, most of which simply assume the ideal inputs without considering the degradation of the real world, such as image blur. In presence of such situation, the traditional matching methods 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 image matching will be reduced by the defective output of the image restoration. In this paper, we propose a joint 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 blurry 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 restoration, we consider both local and sparse information and adopt distance-weighted sparse representation to obtain better representation coefficients. By iteratively restoring the input image in pursuit of the sparest representation, our approach can achieve restoration and matching simultaneous, and these two tasks can benefit greatly from each other. matching, we give a coarse to fine matching strategy to further improve the matching accuracy. Experiments demonstrate the effectiveness of our method compared with conventional methods.