As-Aligned-As-Possible Image Stitching Based on Deviation-Corrected Warping With Global Similarity Constraints

Handling local misalignment caused by the local warp remains a common and challenging task for image stitching. Moreover, the stitched image is prone to appearing ghosting due to the variations of the image viewpoint between images. To solve the problem of local misalignment, we propose a projection deviation-corrected local warping method with a global similarity constraint for image stitching. Recent warps prove that the warp of the local mesh guide image effectively improves the accuracy of image alignment. Geometric projection deviation is well used to accurately correct pixel offsets in image warping. To correct pixel offsets, we first remove the outliers from matching points by using the normal distribution model. The retained matches are more precise and can improve the accuracy of image alignment. Next, we use the local warping model combining local homography and global similarity for image warping. To further address the misalignment problem caused by local warping, we describe the local projection deviation of the local warping model by adopting a three-dimensional mesh interpolation model. Finally, the warped images are blended by a linear smoothing model. Experimental results show that our method outperforms the state-of-the-arts in alignment accuracy, and also provides better visual effects on challenging images.