  High dynamic range (HDR) photography is becoming increasingly popular and
available by DSLR and mobile-phone cameras. While deep neural networks (DNN)
have greatly impacted other domains of image manipulation, their use for HDR
tone-mapping is limited due to the lack of a definite notion of ground-truth
solution, which is needed for producing training data.
  In this paper we describe a new tone-mapping approach guided by the distinct
goal of producing low dynamic range (LDR) renditions that best reproduce the
visual characteristics of native LDR images. This goal enables the use of an
unpaired adversarial training based on unrelated sets of HDR and LDR images,
both of which are widely available and easy to acquire.
  In order to achieve an effective training under this minimal requirements, we
introduce the following new steps and components: (i) a range-normalizing
pre-process which estimates and applies a different level of curve-based
compression, (ii) a loss that preserves the input content while allowing the
network to achieve its goal, and (iii) the use of a more concise discriminator
network, designed to promote the reproduction of low-level attributes native
LDR possess.
  Evaluation of the resulting network demonstrates its ability to produce
photo-realistic artifact-free tone-mapped images, and state-of-the-art
performance on different image fidelity indices and visual distances.
