  Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR)
image, which is the so-called inverse tone mapping (iTM), is an important
imaging technique to improve visual effects of imaging devices. In this paper,
we propose a novel deep learning-based iTM method, which learns an inverse tone
mapping network with a generative adversarial regularizer. In the framework of
alternating optimization, we learn a U-Net-based HDR image generator to
transfer input LDR images to HDR ones, and a simple CNN-based discriminator to
classify the real HDR images and the generated ones. Specifically, when
learning the generator we consider the content-related loss and the generative
adversarial regularizer jointly to improve the stability and the robustness of
the generated HDR images. Using the learned generator as the proposed inverse
tone mapping network, we achieve superior iTM results to the state-of-the-art
methods consistently.
