  Stack-based high dynamic range (HDR) imaging is a technique for achieving a
larger dynamic range in an image by combining several low dynamic range images
acquired at different exposures. Minimizing the set of images to combine, while
ensuring that the resulting HDR image fully captures the scene's irradiance, is
important to avoid long image acquisition and post-processing times. The
problem of selecting the set of images has received much attention. However,
existing methods either are not fully automatic, can be slow, or can fail to
fully capture more challenging scenes. In this paper, we propose a fully
automatic method for selecting the set of exposures to acquire that is both
fast and more accurate. We show on an extensive set of benchmark scenes that
our proposed method leads to improved HDR images as measured against ground
truth using the mean squared error, a pixel-based metric, and a visible
difference predictor and a quality score, both perception-based metrics.
