Cross-cycle Transformer-based Stitching Method for Low-resolution Borehole Images

The stitching of borehole images has an important predictive role in safety analysis in the field of geotechnical engineering and intelligent geological exploration. Applying traditional image stitching methods that designed specifically for high-resolution images to low-resolution images will lead to blurred stitching results, stitching seams, fewer matched feature points and difficulties in massive image stitching. To address these problems, we propose an autoencoder-based coarse-to-fine feature extraction network, which can extract image features with high semantic and improves the accuracy of the feature point matching. Besides, we design a cross-cycle Transformer-based image stitching framework, which increase the number of matching feature points by Cross-QuadTree attention and stitch image by affine transformation. Experimental results show that the proposed method can effectively stitch low-resolution geotechnical borehole images with satisfactory visual quality.