  The widespread usage of high-definition screens on edge devices stimulates a
strong demand for efficient high dynamic range (HDR) algorithms. However, many
existing HDR methods either deliver unsatisfactory results or consume too much
computational and memory resources, hindering their application to
high-resolution images (usually with more than 12 megapixels) in practice. In
addition, existing HDR dataset collection methods often are labor-intensive. In
this work, in a new aspect, we discover an excellent opportunity for HDR
reconstructing directly from raw images and investigating novel neural network
structures that benefit the deployment of mobile devices. Our key insights are
threefold: (1) we develop a lightweight-efficient HDR model, RepUNet, using the
structural re-parameterization technique to achieve fast and robust HDR; (2) we
design a new computational raw HDR data formation pipeline and construct a
real-world raw HDR dataset, RealRaw-HDR; (3) we propose a plug-and-play motion
alignment loss to mitigate motion ghosting under limited bandwidth conditions.
Our model contains less than 830K parameters and takes less than 3 ms to
process an image of 4K resolution using one RTX 3090 GPU. While being highly
efficient, our model also outperforms the state-of-the-art HDR methods in terms
of PSNR, SSIM, and a color difference metric.
