A comprehensive review of past and present image inpainting methods

Images can be described as visual representations or likeness of something (person or object) which can be reproduced or captured, e.g. a hand drawing, photographic material. However, for images on photographic material, images can have defects at the point of captured, become damaged, or degrade over time. Historically, these were restored by hand to maintain image quality using a process known as inpainting. The advent of the digital age has seen the rapid shift image storage technologies, from hard-copies to digitalised units in a less burdensome manner with the application of digital tools. This paper presents a comprehensive review of image inpainting methods over the past decade and the commonly used performance metrics and datasets. To increase the clarity of our review, we use a hierarchical representation for the past state-of-the-art traditional methods and the present state-of-the-art deep learning methods. For traditional methods, we divide the techniques into five sub-categories, i.e. Exemplar-based texture synthesis, Exemplar-based structure synthesis, Diffusion-based methods, Sparse representation methods and Hybrid methods. Then we review the deep learning methods, i.e. Convolutional Neural Networks and Generative Adversarial Networks. We detail the strengths and weaknesses of each to provide new insights in the field. To address the challenges raised from our findings, we outline some potential future works.

Automated summary

This paper provides a comprehensive review of image inpainting methods over the past decade and the commonly used performance metrics and datasets. It presents a hierarchical representation for traditional methods and deep learning methods, offering new insights in the field.