Image Stitching Method Based on Global and Local Features

To solve the error accumulation problem in the traditional sequential image stitching algorithm, a new image stitching method was proposed based on global and local features. Both one global image with large field of view and low resolution and some local images with small field of view and high resolution were taken simultaneously. Then, substituting deep learning for the traditional algorithm, the matching points of the two were extracted. And according to their area ratio, the matching point coordinates of the global image were scaled up at the same proportion for the purpose of projecting local images to the plane of the global image without scaling. Finally, the overlapping areas of local images after projection were fused and stitched to form a panoramic image with large field of view and high resolution. Experimental results show that deep learning can achieve feature matching quickly and accurately. Moreover, the local images are independent of each other, effectively solving the restriction of stitching sequence and the accumulation of stitching errors.