  The paper proposes a method to effectively fuse multi-exposure inputs and
generate 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 images play a leading role in generating reasonable
HDR images. Datasets without ground truth are hard to be applied 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 the
paired dataset and learns the mapping from the LDR domain to the 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 the
modified GAN loss, the improved discriminator network and the useful
initialization phase. The proposed method preserves the details of important
regions and improves the total image perceptual quality. Qualitative and
quantitative comparisons against the representative methods demonstrate the
superiority of the proposed UPHDR-GAN.
