  Image composition is an important operation to create visual content. Among
image composition tasks, image blending aims to seamlessly blend an object from
a source image onto a target image with lightly mask adjustment. A popular
approach is Poisson image blending, which enforces the gradient domain
smoothness in the composite image. However, this approach only considers the
boundary pixels of target image, and thus can not adapt to texture of target
image. In addition, the colors of the target image often seep through the
original source object too much causing a significant loss of content of the
source object. We propose a Poisson blending loss that achieves the same
purpose of Poisson image blending. In addition, we jointly optimize the
proposed Poisson blending loss as well as the style and content loss computed
from a deep network, and reconstruct the blending region by iteratively
updating the pixels using the L-BFGS solver. In the blending image, we not only
smooth out gradient domain of the blending boundary but also add consistent
texture into the blending region. User studies show that our method outperforms
strong baselines as well as state-of-the-art approaches when placing objects
onto both paintings and real-world images.
