High Dynamic Range Imaging of Dynamic Scenes with Saturation
  Compensation but without Explicit Motion Compensation
Haesoo Chung Nam Ik Cho
High dynamic range (HDR) imaging is a highly challenging task since a large
amount of information is lost due to the limitations of camera sensors. For HDR
imaging, some methods capture multiple low dynamic range (LDR) images with
altering exposures to aggregate more information. However, these approaches
introduce ghosting artifacts when significant inter-frame motions are present.
Moreover, although multi-exposure images are given, we have little information
in severely over-exposed areas. Most existing methods focus on motion
compensation, i.e., alignment of multiple LDR shots to reduce the ghosting
artifacts, but they still produce unsatisfying results. These methods also
rather overlook the need to restore the saturated areas. In this paper, we
generate well-aligned multi-exposure features by reformulating a motion
alignment problem into a simple brightness adjustment problem. In addition, we
propose a coarse-to-fine merging strategy with explicit saturation
compensation. The saturated areas are reconstructed with similar well-exposed
content using adaptive contextual attention. We demonstrate that our method
outperforms the state-of-the-art methods regarding qualitative and quantitative
evaluations.