  Exposure correction is one of the fundamental tasks in image processing and
computational photography. While various methods have been proposed, they
either fail to produce visually pleasing results, or only work well for limited
types of image (e.g., underexposed images). In this paper, we present a novel
automatic exposure correction method, which is able to robustly produce
high-quality results for images of various exposure conditions (e.g.,
underexposed, overexposed, and partially under- and over-exposed). At the core
of our approach is the proposed dual illumination estimation, where we
separately cast the under- and over-exposure correction as trivial illumination
estimation of the input image and the inverted input image. By performing dual
illumination estimation, we obtain two intermediate exposure correction results
for the input image, with one fixes the underexposed regions and the other one
restores the overexposed regions. A multi-exposure image fusion technique is
then employed to adaptively blend the visually best exposed parts in the two
intermediate exposure correction images and the input image into a globally
well-exposed image. Experiments on a number of challenging images demonstrate
the effectiveness of the proposed approach and its superiority over the
state-of-the-art methods and popular automatic exposure correction tools.
