  Single-image HDR reconstruction or inverse tone mapping (iTM) is a
challenging task. In particular, recovering information in over-exposed regions
is extremely difficult because details in such regions are almost completely
lost. In this paper, we present a deep learning based iTM method that takes
advantage of the feature extraction and mapping power of deep convolutional
neural networks (CNNs) and uses a lightness prior to modulate the CNN to better
exploit observations in the surrounding areas of the over-exposed regions to
enhance the quality of HDR image reconstruction. Specifically, we introduce a
Hierarchical Synthesis Network (HiSN) for inferring a HDR image from a LDR
input and a Lightness Adpative Modulation Network (LAMN) to incorporate the the
lightness prior knowledge in the inferring process. The HiSN hierarchically
synthesizes the high-brightness component and the low-brightness component of
the HDR image whilst the LAMN uses a lightness adaptive mask that separates
detail-less saturated bright pixels from well-exposed lower light pixels to
enable HiSN to better infer the missing information, particularly in the
difficult over-exposed detail-less areas. We present experimental results to
demonstrate the effectiveness of the new technique based on quantitative
measures and visual comparisons. In addition, we present ablation studies of
HiSN and visualization of the activation maps inside LAMN to help gain a deeper
understanding of the internal working of the new iTM algorithm and explain why
it can achieve much improved performance over state-of-the-art algorithms.
