  Reconstruction of high-quality HDR images is at the core of modern
computational photography. Significant progress has been made with multi-frame
HDR reconstruction methods, producing high-resolution, rich and accurate color
reconstructions with high-frequency details. However, they are still prone to
fail in dynamic or largely over-exposed scenes, where frame misalignment often
results in visible ghosting artifacts. Recent approaches attempt to alleviate
this by utilizing an event-based camera (EBC), which measures only binary
changes of illuminations. Despite their desirable high temporal resolution and
dynamic range characteristics, such approaches have not outperformed
traditional multi-frame reconstruction methods, mainly due to the lack of color
information and low-resolution sensors. In this paper, we propose to leverage
both bracketed LDR images and simultaneously captured events to obtain the best
of both worlds: high-quality RGB information from bracketed LDRs and
complementary high frequency and dynamic range information from events. We
present a multi-modal end-to-end learning-based HDR imaging system that fuses
bracketed images and event modalities in the feature domain using attention and
multi-scale spatial alignment modules. We propose a novel event-to-image
feature distillation module that learns to translate event features into the
image-feature space with self-supervision. Our framework exploits the higher
temporal resolution of events by sub-sampling the input event streams using a
sliding window, enriching our combined feature representation. Our proposed
approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic
and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the
HdM HDR dataset, respectively.
