Research on seamless image stitching based on fast marching method

Abstract

Image stitching is an important way to achieve large‐field high‐resolution imaging. The inconsistencies in brightness and structure and defects in ghosting, blurring and misalignment between images, which are inevitable and difficult to eliminate, make a challenge to image stitching, due to the external lighting environment and changes in camera pose and parameters. Here, a novel method is proposed to search for the optimal seamline based on the fast marching method, which can stitch large parallax images with high quality. A feature weight map is first formed based on the similarity in colour, edge, texture and saliency of the images. Then it is used as the cost value of the seamline to search for the optimal seamline by fast marching method. The results show that this new method is more efficient to reduce defects, such as ghosting, misalignment and chromatic aberration, and realize high quality image stitching compared with traditional stitching tools and methods, which provides a new perspective for image stitching technology.

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“…The first idea is to avoid the generation of gaps, and the optimal stitch line method is one of the representative methods [15], such as the snake model [16], Dijkstra's algorithm [17,18], the dynamic programming (DP) algorithm [19,20], graph cut algorithms [21,22] and ant colony optimization [23]. In addition, with the rapid development of deep learning convolutional neural networks (CNNs) over the last few years [24][25][26][27][28], Li et al [29] proposed to combine the semantic segmentation information of CNNs [30,31] to calculate the difference and search for the optimal stitching seam.…”

“…However, significant gaps in the stitched areas often require further processing as pixels are rearranged during the image rectification process. The usual method [15][16][17][18][19][20][21][22] is to re-segment the image by solving the difference optimization function for the overlapping region of the image, but the segmentation quality of this method is limited by the degree of overlap between frames and the accuracy of the alignment. At the same time, it ignores the information about the boundaries of the front and back frames.…”