  Presenting context images to a viewer's peripheral vision is one of the most
effective techniques to enhance immersive visual experiences. However, most
images only present a narrow view, since the field-of-view (FoV) of standard
cameras is small. To overcome this limitation, we propose a deep learning
approach that learns to predict a 180{\deg} panoramic image from a narrow-view
image. Specifically, we design a foveated framework that applies different
strategies on near-periphery and mid-periphery regions. Two networks are
trained separately, and then are employed jointly to sequentially perform
narrow-to-90{\deg} generation and 90{\deg}-to-180{\deg} generation. The
generated outputs are then fused with their aligned inputs to produce expanded
equirectangular images for viewing. Our experimental results show that
single-view-to-panoramic image generation using deep learning is both feasible
and promising.
