  Capturing real world lighting is a long standing challenge in imaging and
most practical methods acquire High Dynamic Range (HDR) images by either fusing
multiple exposures, or boosting the dynamic range of Standard Dynamic Range
(SDR) images. Multiple exposure capture is problematic as it requires longer
capture times which can often lead to ghosting problems. The main alternative,
inverse tone mapping is an ill-defined problem that is especially challenging
as single captured exposures usually contain clipped and quantized values, and
are therefore missing substantial amounts of content. To alleviate this, we
propose a new approach, High Dynamic Range Thermal (HDRT), for HDR acquisition
using a separate, commonly available, thermal infrared (IR) sensor. We propose
a novel deep neural method (HDRTNet) which combines IR and SDR content to
generate HDR images. HDRTNet learns to exploit IR features linked to the RGB
image and the IR-specific parameters are subsequently used in a dual branch
method that fuses features at shallow layers. This produces an HDR image that
is significantly superior to that generated using naive fusion approaches. To
validate our method, we have created the first HDR and thermal dataset, and
performed extensive experiments comparing HDRTNet with the state-of-the-art. We
show substantial quantitative and qualitative quality improvements on both
over- and under-exposed images, showing that our approach is robust to
capturing in multiple different lighting conditions.
