  Most consumer-grade digital cameras can only capture a limited range of
luminance in real-world scenes due to sensor constraints. Besides, noise and
quantization errors are often introduced in the imaging process. In order to
obtain high dynamic range (HDR) images with excellent visual quality, the most
common solution is to combine multiple images with different exposures.
However, it is not always feasible to obtain multiple images of the same scene
and most HDR reconstruction methods ignore the noise and quantization loss. In
this work, we propose a novel learning-based approach using a spatially dynamic
encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single
image HDR reconstruction with denoising and dequantization. The network
consists of a UNet-style base network to make full use of the hierarchical
multi-scale information, a condition network to perform pattern-specific
modulation and a weighting network for selectively retaining information.
Moreover, we propose a Tanh_L1 loss function to balance the impact of
over-exposed values and well-exposed values on the network learning. Our method
achieves the state-of-the-art performance in quantitative comparisons and
visual quality. The proposed HDRUNet model won the second place in the single
frame track of NITRE2021 High Dynamic Range Challenge.
