  Despite significant advancements in network-based image harmonization
techniques, there still exists a domain disparity between typical training
pairs and real-world composites encountered during inference. Most existing
methods are trained to reverse global edits made on segmented image regions,
which fail to accurately capture the lighting inconsistencies between the
foreground and background found in composited images. In this work, we
introduce a self-supervised illumination harmonization approach formulated in
the intrinsic image domain. First, we estimate a simple global lighting model
from mid-level vision representations to generate a rough shading for the
foreground region. A network then refines this inferred shading to generate a
harmonious re-shading that aligns with the background scene. In order to match
the color appearance of the foreground and background, we utilize ideas from
prior harmonization approaches to perform parameterized image edits in the
albedo domain. To validate the effectiveness of our approach, we present
results from challenging real-world composites and conduct a user study to
objectively measure the enhanced realism achieved compared to state-of-the-art
harmonization methods.
