  Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range
(HDR) information from Low Dynamic Range (LDR) image content. The dynamic range
of well-exposed areas must be expanded and any missing information due to
over/under-exposure must be recovered (hallucinated). The majority of methods
focus on the former and are relatively successful, while most attempts on the
latter are not of sufficient quality, even ones based on Convolutional Neural
Networks (CNNs). A major factor for the reduced inpainting quality in some
works is the choice of loss function. Work based on Generative Adversarial
Networks (GANs) shows promising results for image synthesis and LDR inpainting,
suggesting that GAN losses can improve inverse tone mapping results. This work
presents a GAN-based method that hallucinates missing information from badly
exposed areas in LDR images and compares its efficacy with alternative
variations. The proposed method is quantitatively competitive with
state-of-the-art inverse tone mapping methods, providing good dynamic range
expansion for well-exposed areas and plausible hallucinations for saturated and
under-exposed areas. A density-based normalisation method, targeted for HDR
content, is also proposed, as well as an HDR data augmentation method targeted
for HDR hallucination.
