  Color image inpainting is a challenging task in imaging science. The existing
method is based on real operation, and the red, green and blue channels of the
color image are processed separately, ignoring the correlation between each
channel. In order to make full use of the correlation between each channel,
this paper proposes a Quaternion Generative Adversarial Neural Network (QGAN)
model and related theory, and applies it to solve the problem of color image
inpainting with large area missing. Firstly, the definition of quaternion
deconvolution is given and the quaternion batch normalization is proposed.
Secondly, the above two innovative modules are applied to generate adversarial
networks to improve stability. Finally, QGAN is applied to color image
inpainting and compared with other state-of-the-art algorithms. The
experimental results show that QGAN has superiority in color image inpainting
with large area missing.
