# Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks

## TL;DR

This paper introduces an improved Wasserstein GAN for semantic image inpainting, combining learned semantic features with an optimization-based approach to recover missing image regions with high realism.

## Contribution

The work develops a new GAN architecture to learn a semantic latent space and integrates it into an optimization framework for enhanced image inpainting.

## Key findings

- Achieves high-quality, photo-realistic inpainting results.
- Effectively recovers large missing regions using semantic information.
- Outperforms existing methods in qualitative and quantitative evaluations.

## Abstract

Image inpainting is the task of filling-in missing regions of a damaged or incomplete image. In this work we tackle this problem not only by using the available visual data but also by incorporating image semantics through the use of generative models. Our contribution is twofold: First, we learn a data latent space by training an improved version of the Wasserstein generative adversarial network, for which we incorporate a new generator and discriminator architecture. Second, the learned semantic information is combined with a new optimization loss for inpainting whose minimization infers the missing content conditioned by the available data. It takes into account powerful contextual and perceptual content inherent in the image itself. The benefits include the ability to recover large regions by accumulating semantic information even it is not fully present in the damaged image. Experiments show that the presented method obtains qualitative and quantitative top-tier results in different experimental situations and also achieves accurate photo-realism comparable to state-of-the-art works.