Abstract. This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with
large-scale foreground motions. In state-of-the-art deep HDR imaging, input images are first aligned using optical flows before merging,
which are still error-prone due to occlusion and large motions. In stark
contrast to flow-based methods, we formulate HDR imaging as an image translation problem without optical flows. Moreover, our simple
translation network can automatically hallucinate plausible HDR details
in the presence of total occlusion, saturation and under-exposure, which
are otherwise almost impossible to recover by conventional optimization
approaches. Our framework can also be extended for different reference
images. We performed extensive qualitative and quantitative comparisons to show that our approach produces excellent results where color
artifacts and geometric distortions are significantly reduced compared
to existing state-of-the-art methods, and is robust across various inputs,
including images without radiometric calibration