  In this paper, we propose a method to add constraints that are
un-formulatable in generative adversarial networks (GAN)-based arbitrary size
RAW Bayer image generation. It is shown theoretically that by using the
transformed data in GAN training, it is able to improve the learning of the
original data distribution, owing to the invariant of Jensen-Shannon (JS)
divergence between two distributions under invertible and differentiable
transformation. Benefiting from the proposed method, RAW Bayer pattern images
can be generated by configuring the transformation as demosaicing. It is shown
that by adding another transformation, the proposed method is able to
synthesize high-quality RAW Bayer images with arbitrary size. Experimental
results show that images generated by the proposed method outperform the
existing methods in the Fr\'echet inception distance (FID) score, peak signal
to noise ratio (PSNR), and mean structural similarity (MSSIM), and the training
process is more stable. To the best knowledge of the authors, there is no
open-source, large-scale image dataset in the RAW Bayer domain, which is
crucial for research works aiming to explore the image signal processing (ISP)
pipeline design for computer vision tasks. Converting the existing commonly
used color image datasets to their corresponding RAW Bayer versions, the
proposed method can be a promising solution to the RAW image dataset problem.
We also show in the experiments that, by training object detection frameworks
using the synthesized RAW Bayer images, they can be used in an end-to-end
manner (from RAW images to vision tasks) with negligible performance
degradation.
