Semantic inpainting or image completion alludes to the task of inferring arbitrary large missing regions in images based on image semantics. Since the prediction of image pixels requires an indication of high-level context, this makes it significantly tougher than image completion, which is often more concerned with correcting data corruption and removing entire objects from the input image. On the other hand, image enhancement attempts to eliminate unwanted noise and blur from the image, along with sustaining most of the image details. Efficient image completion and enhancement model should be able to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image. Generative Adversarial Networks (GAN), have turned out to be helpful in picture completion tasks. In this chapter, we will discuss the underlying GAN architecture and how they can be used used for image completion tasks.
@article{arxiv.2307.14748,
title = {Semantic Image Completion and Enhancement using GANs},
author = {Priyansh Saxena and Raahat Gupta and Akshat Maheshwari and Saumil Maheshwari},
journal= {arXiv preprint arXiv:2307.14748},
year = {2023}
}
Comments
This work is part of 'High-Performance Vision Intelligence'; Part of the Studies in Computational Intelligence book series (SCI, volume 913) and can be accessed at: https://link.springer.com/chapter/10.1007/978-981-15-6844-2_11. arXiv admin note: substantial text overlap with arXiv:1911.02222