  Deep learning techniques have made considerable progress in image inpainting,
restoration, and reconstruction in the last few years. Image outpainting, also
known as image extrapolation, lacks attention and practical approaches to be
fulfilled, owing to difficulties caused by large-scale area loss and less
legitimate neighboring information. These difficulties have made outpainted
images handled by most of the existing models unrealistic to human eyes and
spatially inconsistent. When upsampling through deconvolution to generate fake
content, the naive generation methods may lead to results lacking
high-frequency details and structural authenticity. Therefore, as our novelties
to handle image outpainting problems, we introduce structural prior as a
condition to optimize the generation quality and a new semantic embedding term
to enhance perceptual sanity. we propose a deep learning method based on
Generative Adversarial Network (GAN) and condition edges as structural prior in
order to assist the generation. We use a multi-phase adversarial training
scheme that comprises edge inference training, contents inpainting training,
and joint training. The newly added semantic embedding loss is proved effective
in practice.
