In this post, we are going to understand the underlying concepts of semantic image inpainting and various techniques developed for the same. However, we are going to focus on an effective and elegant state-of-the-art context encoder and generative model based approach for image inpainting.
Let’s get going!
The model and architecture discussed in this post is referred from the research paper link given below. Kindly refer to the research paper for the complete implementation:
What is Semantic Image Inapainting?
Imagine having an old but precious photograph and finding out a few parts of it had corrupted. You want to cherish the moment captured by the photograph forever and recover the image. Image inpainting can save the day!
Given a corrupted image with parts of the image missing or distorted, semantic image inpainting refers to the filling up of these regions with the help of the available visual data. Semantic image inpainting, as its name suggests, also takes into account the context of the image and pixels surrounding the missing regions while filling these regions.
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