4 - Deep Learning Inpainting Methods

Summary

Chapter 4 describes the rise of deep learning inpainting methods in the past ten years. These methods learn an end-to-end mapping from a corrupted input to its estimated restoration. In contrast with traditional methods from the previous chapters, which use model-based or hand-crafted features, learning-based algorithms are able to infer the missing content by training on a large-scale dataset and can capture local or non-local dependencies inside the image and over the full dataset and exploit high-level information inherent in the image itself. In this chapter we present the seminal deep learning inpainting methods up to 2020 together with dedicated datasets designed for the inpainting problem.