  Image inpainting is the process of regenerating lost parts of the image.
Supervised algorithm-based methods have shown excellent results but have two
significant drawbacks. They do not perform well when tested with unseen data.
They fail to capture the global context of the image, resulting in a visually
unappealing result. We propose a novel self-supervised learning framework for
image-inpainting: Weighted Self-Supervised Learning (WSSL) to tackle these
problems. We designed WSSL to learn features from multiple weighted pretext
tasks. These features are then utilized for the downstream task,
image-inpainting. To improve the performance of our framework and produce more
visually appealing images, we also present a novel loss function for image
inpainting. The loss function takes advantage of both reconstruction loss and
perceptual loss functions to regenerate the image. Our experimentation shows
WSSL outperforms previous methods, and our loss function helps produce better
results.
