  In image editing, the most common task is pasting objects from one image to
the other and then eventually adjusting the manifestation of the foreground
object with the background object. This task is called image compositing. But
image compositing is a challenging problem that requires professional editing
skills and a considerable amount of time. Not only these professionals are
expensive to hire, but the tools (like Adobe Photoshop) used for doing such
tasks are also expensive to purchase making the overall task of image
compositing difficult for people without this skillset. In this work, we aim to
cater to this problem by making composite images look realistic. To achieve
this, we are using Generative Adversarial Networks (GANS). By training the
network with a diverse range of filters applied to the images and special loss
functions, the model is able to decode the color histogram of the foreground
and background part of the image and also learns to blend the foreground object
with the background. The hue and saturation values of the image play an
important role as discussed in this paper. To the best of our knowledge, this
is the first work that uses GANs for the task of image compositing. Currently,
there is no benchmark dataset available for image compositing. So we created
the dataset and will also make the dataset publicly available for benchmarking.
Experimental results on this dataset show that our method outperforms all
current state-of-the-art methods.
