Image matching of Gaussian blurred image based on SIFT algorithm

By analyzing the algorithm of Scale Invariant Feature Transition (SIFT), in the process of the experiments, we found, the original image will become serious fuzzy after Gaussian smoothing. At this time, if we do matching directly using the algorithm of SIFT, the matching results will produce many wrong matching points, the number of the feature points successfully matched will be reduced. We found the main reason is that the image is serious blurred by the Gaussian smoothing. In the process of smoothing, the edge points and the pixels whose gray value changed largely are also smoothed, then leading to the number of feature points reduced. Through the analysis of the experiment, we found, if we use the Laplace operator to process the blurred image before matching. This can enhance the characteristics of edge. Then, using SIFT to match image. This method is better than using SIFT directly. The number of feature points is increased significantly. So, this method can improve the probability of a success matching.