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.

图像匹配是图像处理领域中的一个经典问题，其目的是定位实时图像的唯一位置，并且已被广泛用于基于视觉的导航系统中。但是，大多数有关图像匹配的工作只是假设了理想的输入，而没有考虑现实世界的退化，例如图像模糊。在这种情况下，传统的匹配方法通常首先求助于图像还原，然后对还原后的图像进行图像匹配。然而，通过分别处理恢复和匹配，图像恢复的有缺陷输出将降低模糊图像匹配的准确性。本文提出了一种基于距离加权的稀疏表示（JRM-DSR）的联合模糊图像复原和匹配方法，在利用恢复和匹配之间的相关性之前，先利用稀疏表示。该先验假设模糊图像如果被正确恢复，则可以很好地表示为由参考图像构成的字典的稀疏线性组合。为了获得更准确的匹配结果以帮助图像恢复，我们考虑了局部信息和稀疏信息，并采用距离加权的稀疏表示以获得更好的表示系数。通过以最备用的表示方式迭代还原输入图像，我们的方法可以同时实现还原和匹配，并且这两个任务可以彼此受益匪浅。此外，提出了一种从粗到精的匹配策略，以进一步提高匹配精度和搜索效率。