Structure-Aware Image Compositing

The classic task of image compositing is complicated by the fact that the

source and target images need to be carefully aligned and adjusted. Otherwise,

it is not possible to achieve convincing results. Visual artifacts are

caused by image intensity mismatch, image distortion or structure misalignment

even if the images have been globally aligned. In this paper we extend

classic Poisson blending by a constrained structure deformation and propagation

method. This approach can solve the above-mentioned problems

and proves useful for a variety of applications, e.g. in de-ghosting of mosaic

images, classic image compositing or other applications such as superresolution

from image databases. Our method is based on the following basic

steps. First, an optimal partitioning boundary is computed between the input

images. Then, features along this boundary are robustly aligned and

deformation vectors are computed. Starting at these features, salient edges

are traced and aligned, serving as additional constraints for the smooth deformation

field, which is propagated robustly and smoothly into the interior

of the target image. If very different images are to be stitched, we propose

to base the deformation constraints on the curvature of the salient edges

for C1-continuity of the structures between the images. We present results

that show the robustness of our method on a number of image stitching and

compositing tasks.