Abstract: An image stitching method based on Scale-Invariant Feature Transform (SIFT), thin-plate spline function and power function was proposed to solve the problem of low efficiency, mismatching of feature points, ghosting and stitching seam in image stitching algorithm. The point mapping relationship and overlapping area between the images were calculated by sampling and matching the input images. The local distortion model of the image was calculated by the feature point set, and the deformation of the image was completed by image interpolation. The power function weighting model was used to realize smooth transaction of the pixels in the deformed image to complete the image stitching. Experimental results show that the proposed method improves the registration efficiency of the feature points approximately by 59.78% and obtains more pairs of feature points compared to the traditional SIFT algorithm. Moreover, compared with the classical image stitching algorithm, the method solves the problems of image ghosting and stitching seam, and improves the score of image quality evaluation index.

Key words: image stitching, multi-resolution fusion, ghosting, image deformation, Scale-Invariant Feature Transform (SIFT), weight

摘要： 针对图像拼接算法存在效率低下、特征点错误匹配、重影和拼接缝等问题，提出一种基于尺度不变特征变换、薄板样条函数和幂函数的图像拼接方法。该方法通过对输入图像进行采样匹配，计算输入图像间的点映射关系和重合区域，使用点映射关系对重合区域内的特征点进行定向配准，利用特征点集合计算出图像的局部扭曲模型，使用图像插值方法对图像进行变形映射；采用幂函数权重模型对变形图像中的像素进行平滑过渡，完成图像拼接。实验结果表明，在拼接相同图像的情况下，所提方法与传统的尺度不变特征变换算法相比，特征点配准效率提高了约59.78%，而且得到了更多的特征点对；与经典的图像拼接算法相比，该方法解决了图像的重影和拼接缝的问题，同时提高了图像的质量评估指标的得分。

关键词: 图像拼接, 多分辨率融合, 重影, 图像变形, 尺度不变特征变换, 权重