  Image harmonization aims to generate a more realistic appearance of
foreground and background for a composite image. Existing methods perform the
same harmonization process for the whole foreground. However, the implanted
foreground always contains different appearance patterns. All the existing
solutions ignore the difference of each color block and losing some specific
details. Therefore, we propose a novel global-local two stages framework for
Fine-grained Region-aware Image Harmonization (FRIH), which is trained
end-to-end. In the first stage, the whole input foreground mask is used to make
a global coarse-grained harmonization. In the second stage, we adaptively
cluster the input foreground mask into several submasks by the corresponding
pixel RGB values in the composite image. Each submask and the coarsely adjusted
image are concatenated respectively and fed into a lightweight cascaded module,
adjusting the global harmonization performance according to the region-aware
local feature. Moreover, we further designed a fusion prediction module by
fusing features from all the cascaded decoder layers together to generate the
final result, which could utilize the different degrees of harmonization
results comprehensively. Without bells and whistles, our FRIH algorithm
achieves the best performance on iHarmony4 dataset (PSNR is 38.19 dB) with a
lightweight model. The parameters for our model are only 11.98 M, far below the
existing methods.
