Abstract
The traditional image stitching method has some shortcomings such as double shadow, chromatic aberration, and stitching. In view of this, this paper proposes a power function-weighted image stitching method that combines SURF optimization and improved cell acceleration. First, the method uses the cosine similarity to preliminarily judge the similarity of the feature points and then uses the two-way consistency mutual selection to filter the feature point pairs again. Simultaneously, some incorrect matching points in the reverse matching are eliminated. Finally, the method uses the MSAC algorithm to perform fine matching. Then, the power function-weighted fusion algorithm is used to calculate the weight of the center point. The power function weight of the accelerated cell is used to perform the final image fusion. The experimental results show that the matching accuracy rate of the proposed method is about 11 percentage points higher than the traditional SURF algorithm, and the time is reduced by about 1.6 s. In the image fusion stage, this paper first selects images with different brightness, angles, resolutions, and scales to verify the effectiveness of the proposed method. The results show that the proposed method effectively solves the ghosting and stitching seams. Comparing with the traditional fusion algorithm, the time consumption is reduced by at least 2 s, the mean square error is reduced by about 1.32%∼1.48%, and the information entropy is improved by about 0.98%∼1.70%. The proposed method has better performance in matching accuracy and fusion effect and has better stitching quality.
Highlights
Image stitching technology includes image registration and fusion. e target of image stitching is to form a wide viewing angle and seamless panoramic image from multiple partially overlapping images
Ere are many ways to extract feature points, such as Harris, scale-invariant feature transform (SIFT), SURF, FAST, and ORB. e SURF algorithm can adapt to image rotation and zooming and maintain stability under the conditions of viewing angle changes, scale changes, and illumination changes. erefore, the proposed method uses the SURF algorithm [3] to extract feature points. e SURF algorithm is an improvement algorithm of the SIFT, and it is not affected by the image rotation, scale scaling, and brightness changes
Experimental Results and Analysis e experiment in this paper uses Matlab 2014 for programming and image stitching under a Windows 10 system with an Intel i5 processor and a memory of 12G, which verifies the feasibility and superiority of the calculation in this paper. e first is the comparison between the traditional SURF algorithm and the proposed method in the image registration stage, and the second is the comparison between the image fusion based on this algorithm and the other two algorithms in the image fusion stage
Summary
E traditional image stitching method has some shortcomings such as double shadow, chromatic aberration, and stitching. This paper proposes a power function-weighted image stitching method that combines SURF optimization and improved cell acceleration. The method uses the MSAC algorithm to perform fine matching. En, the power function-weighted fusion algorithm is used to calculate the weight of the center point. E power function weight of the accelerated cell is used to perform the final image fusion. E experimental results show that the matching accuracy rate of the proposed method is about 11 percentage points higher than the traditional SURF algorithm, and the time is reduced by about 1.6 s. In the image fusion stage, this paper first selects images with different brightness, angles, resolutions, and scales to verify the effectiveness of the proposed method. E proposed method has better performance in matching accuracy and fusion effect and has better stitching quality Comparing with the traditional fusion algorithm, the time consumption is reduced by at least 2 s, the mean square error is reduced by about 1.32%∼1.48%, and the information entropy is improved by about 0.98%∼1.70%. e proposed method has better performance in matching accuracy and fusion effect and has better stitching quality
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