  The paper proposes a method to effectively fuse multi-exposure inputs and
generates high-quality high dynamic range (HDR) images with unpaired datasets.
Deep learning-based HDR image generation methods rely heavily on paired
datasets. The ground truth provides information for the network getting HDR
images without ghosting. Datasets without ground truth are hard to apply to
train deep neural networks. Recently, Generative Adversarial Networks (GAN)
have demonstrated their potentials of translating images from source domain X
to target domain Y in the absence of paired examples. In this paper, we propose
a GAN-based network for solving such problems while generating enjoyable HDR
results, named UPHDR-GAN. The proposed method relaxes the constraint of paired
dataset and learns the mapping from LDR domain to HDR domain. Although the pair
data are missing, UPHDR-GAN can properly handle the ghosting artifacts caused
by moving objects or misalignments with the help of modified GAN loss, improved
discriminator network and useful initialization phase. The proposed method
preserves the details of important regions and improves the total image
perceptual quality. Qualitative and quantitative comparisons against other
methods demonstrated the superiority of our method.
