Improving The Harmony Of The Composite Image By Spatial-Separated Attention Module

Image composition is one of the most important applications in image processing. In this work, we start from an empirical observation: the differences can only be found in the spliced region between the spliced image and the harmonized result while they share the same semantic information and the appearance in the non-spliced region. Thus, in order to learn the feature map in the masked region and the others individually, we propose a novel attention module named Spatial-Separated Attention Module (S 2 AM). Furthermore, we design a novel image harmonization framework by inserting the S 2 AM in the coarser low-level features of the Unet structure by two different ways. Besides image harmonization, we make a big step for harmonizing the composite image without the specific mask under previous observation. The experiments show that the proposed S 2 AM performs better than other state-of-the-art attention modules in our task. Moreover, we demonstrate the advantages of our model against other state-of-the-art image harmonization methods via criteria from multiple points of view.