The Effect of Deblurring on Matching of Motion Blurred Remote Sensing Images

Image matching for blurred images is one of the most important and frequently discussed topics. In order to improve the precision of image matching for blurred images, we add a deblurring process before matching. Due to the ill-posedness of the deblurring problem, we use an image sparsity prior combined with patch-wise minimal and maximal pixel of latent image. Half quadratics splitting algorithm is applied under the maximum a posterior (MAP) framework. Six classical feature descriptors are utilized to implement the image matching. Five evaluation indexes are used to test the effect of the proposed deblurring algorithm on motion blurred remote sensing images matching. Experimental results show that the proposed deburring method can impair the influence of motion blur and improve the precision of matching.