  We present a simple yet effective self-supervised pre-training method for
image harmonization which can leverage large-scale unannotated image datasets.
To achieve this goal, we first generate pre-training data online with our
Label-Efficient Masked Region Transform (LEMaRT) pipeline. Given an image,
LEMaRT generates a foreground mask and then applies a set of transformations to
perturb various visual attributes, e.g., defocus blur, contrast, saturation, of
the region specified by the generated mask. We then pre-train image
harmonization models by recovering the original image from the perturbed image.
Secondly, we introduce an image harmonization model, namely SwinIH, by
retrofitting the Swin Transformer [27] with a combination of local and global
self-attention mechanisms. Pre-training SwinIH with LEMaRT results in a new
state of the art for image harmonization, while being label-efficient, i.e.,
consuming less annotated data for fine-tuning than existing methods. Notably,
on iHarmony4 dataset [8], SwinIH outperforms the state of the art, i.e., SCS-Co
[16] by a margin of 0.4 dB when it is fine-tuned on only 50% of the training
data, and by 1.0 dB when it is trained on the full training dataset.
