Advances in Deep Learning Image Inpainting Architectures for the Effective Reconstruction of Damaged Regions- A Systematic Review

Abstract

Image inpainting is the method of reconstructing the missed or damaged region of the picture with certain rules that play a vital role in computer vision applications. Numerous image inpainting approaches have been recently proposed that effectively reconstruct the missed regions. Traditionally the missed regions are restored by schemes that are derived from the diffusion models, exemplar approaches, and sparsity approaches. Different hybrid schemes are derived from these traditional schemes that show reasonable performance in missed region reconstruction. Recently deep learning-based approaches which are derived from the architectures of Generative adversarial networks (GAN), Convolutional neural networks (CNN), Transformers, and U-Net are commonly used for effective image painting. More specifically numerous approaches have been derived using the advantage of GAN and CNN architectures. These schemes focus on the reconstruction of global structures and local texture components of the missed region from the known region. Therefore, the paper provides a review of different traditional, and recent deep-learning schemes.