  Synthesizing high dynamic range (HDR) images from multiple low-dynamic range
(LDR) exposures in dynamic scenes is challenging. There are two major problems
caused by the large motions of foreground objects. One is the severe
misalignment among the LDR images. The other is the missing content due to the
over-/under-saturated regions caused by the moving objects, which may not be
easily compensated for by the multiple LDR exposures. Thus, it requires the HDR
generation model to be able to properly fuse the LDR images and restore the
missing details without introducing artifacts. To address these two problems,
we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR
images from multi-exposed LDR images. To our best knowledge, this work is the
first GAN-based approach for fusing multi-exposed LDR images for HDR
reconstruction. By incorporating adversarial learning, our method is able to
produce faithful information in the regions with missing content. In addition,
we also propose a novel generator network, with a reference-based residual
merging block for aligning large object motions in the feature domain, and a
deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR
images. Experimental results demonstrate that our model achieves
state-of-the-art reconstruction performance over the prior HDR methods on
diverse scenes.
