Open Access
ARTICLE
A 360-Degree Panoramic Image Inpainting Network Using a Cube Map
Seo Woo Han, Doug Young Suh*
Department of Electronic Engineering, Kyung Hee University, Youngin, 17104, South Korea
* Corresponding Author: Doug Young Suh. Email:
Computers, Materials & Continua 2021, 66(1), 213-228. https://doi.org/10.32604/cmc.2020.012223
Received 20 June 2020; Accepted 10 July 2020; Issue published 30 October 2020
Abstract
Inpainting has been continuously studied in the field of computer
vision. As artificial intelligence technology developed, deep learning technology
was introduced in inpainting research, helping to improve performance. Currently,
the input target of an inpainting algorithm using deep learning has been studied
from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree
panoramic image inpainting method using generative adversarial networks
(GANs). The proposed network inputs a 360-degree equirectangular format
panoramic image converts it into a cube map format, which has relatively little
distortion and uses it as a training network. Since the cube map format is used,
the correlation of the six sides of the cube map should be considered. Therefore,
all faces of the cube map are used as input for the whole discriminative network,
and each face of the cube map is used as input for the slice discriminative network
to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and
baseline algorithms.
Keywords
Cite This Article
S. Woo Han and D. Young Suh, "A 360-degree panoramic image inpainting network using a cube map,"
Computers, Materials & Continua, vol. 66, no.1, pp. 213–228, 2021. https://doi.org/10.32604/cmc.2020.012223
Citations