  Nowadays, panoramic images can be easily obtained by panoramic cameras.
However, when the panoramic camera orientation is tilted, a non-upright
panoramic image will be captured. Existing upright adjustment models focus on
how to estimate more accurate camera orientation, and attribute image
reconstruction to offline or post-processing tasks. To this end, we propose an
online end-to-end network for upright adjustment. Our network is designed to
reconstruct the image while finding the angle. Our network consists of three
modules: orientation estimation, LUT online generation, and upright
reconstruction. Direction estimation estimates the tilt angle of the panoramic
image. Then, a converter block with upsampling function is designed to generate
angle to LUT. This module can output corresponding online LUT for different
input angles. Finally, a lightweight generative adversarial network (GAN) aims
to generate upright images from shallow features. The experimental results show
that in terms of angles, we have improved the accuracy of small angle errors.
In terms of image reconstruction, In image reconstruction, we have achieved the
first real-time online upright reconstruction of panoramic images using deep
learning networks.
