The quality of image stitching is measured by the similarity of
the stitched image to each of the input images, and by the
visibility of the seam between the stitched images.
In order to define and get the best possible stitching, we introduce several formal cost functions for the evaluation of the quality of stitching. In these cost functions, the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will optimize these cost functions, overcoming both photometric inconsistencies and geometric misalignments between the stitched images.
This approach is demonstrated in various applications, including generation of panoramic images, object blending, and removal of compression artifacts. Comparisons with existing methods show the benefits of optimizing the measures in the gradient domain.