  In this paper we propose a new problem scenario in image processing,
wide-range image blending, which aims to smoothly merge two different input
photos into a panorama by generating novel image content for the intermediate
region between them. Although such problem is closely related to the topics of
image inpainting, image outpainting, and image blending, none of the approaches
from these topics is able to easily address it. We introduce an effective
deep-learning model to realize wide-range image blending, where a novel
Bidirectional Content Transfer module is proposed to perform the conditional
prediction for the feature representation of the intermediate region via
recurrent neural networks. In addition to ensuring the spatial and semantic
consistency during the blending, we also adopt the contextual attention
mechanism as well as the adversarial learning scheme in our proposed method for
improving the visual quality of the resultant panorama. We experimentally
demonstrate that our proposed method is not only able to produce visually
appealing results for wide-range image blending, but also able to provide
superior performance with respect to several baselines built upon the
state-of-the-art image inpainting and outpainting approaches.
