Shape-optimizing hybrid warping for image stitching

Projective distortion remains an open problem for image stitching. This paper proposed a novel weight-based shape-optimizing warping framework, which combines a projective transformation and a similarity transformation so as to reduce the projective distortion. This method aligned images with the projective transformation, and optimized images' shape under the constraint condition of similarity transformation. By automatically locating the overlapping and non-overlapping regions of input images, the weight of the constraint condition was determined. In addition, to ensure the smoothness of the final stitching result, we specially designed a continuous variation of the weight according to the location information. Since the proposed warping method joints the advantages of both projective transformation and similarity transformation, it can preserve the accuracy of image alignment and alleviate the projective distortion as small as possible. Various experiments demonstrated the effectiveness of our method.