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For ancient paintings and calligraphies, including thangkas and murals, which are often of large format and cannot be scanned at one time, it is necessary to utilize high-precision CIS image sensors to scan the paintings and calligraphies several times in segments, and then finally get the finished images through image stitching. Among them, fast and accurate stitching of large format images with hundreds of millions of pixels is a major difficulty. Due to the huge size, more computational resources are required, and conventional image stitching algorithms can not directly complete the stitching task in the case of limited performance. Therefore, we propose a high-resolution image stitching algorithm OR-SIFT based on deep learning prediction of overlapping regions, which combines the convolutional neural network with the traditional feature detection method. It uses the neural network to predict the overlapping region as the region of interest for subsequent stitching algorithms. Then, an improved SIFT algorithm is used to extract and describe features in the overlapping region, followed by precise stitching. Additionally, a strategy for continuously stitching multiple high-resolution images is proposed to reduce computational complexity while ensuring accuracy, achieving continuous stitching of multiple high-resolution images.
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