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.

本研究介绍了一种在内在图像领域中制定的自我监督照明协调方法，通过估计中级视觉表征生成的大体阴影，再通过网络对其进行修正以生成与背景场景一致的协调再阴影，结合先前协调方法的思想，在色调域中执行参数化图像编辑，实现前景与背景的颜色外观匹配，并通过挑战性实际复合结果和用户研究验证我们方法的有效性和与最先进的协调方法相比增强的真实感。