Image Inpainting and Digital Camouflage: Methods, Applications, and Perspectives for Remote Sensing

Abstract

Image inpainting refers to the process of restoring missing or damaged areas in an image. This research field has been very active in recent years, driven by various applications such as reconstructing lost fragments, concealing data loss in corrupted image transmissions, removing objects in image editing, and interpolating image content for reconstruction in image-based rendering from various fields of view. This article presents existing methods of image inpainting, covering classical approaches, CNN-based methods, and GAN-based methods. In addition, it explores techniques related to steganography, adversarial image synthesis, and false image generation. Examples of applications are provided for each category of image modification methods. Although image inpainting and digital camouflage are not yet widely studied in the remote sensing community, there has been a growing interest in these topics in recent years. To broaden the understanding of these methods, this study also reviews techniques developed in the field of computer science, which have the potential to be adapted for remote sensing applications. The main contribution of this article is the presentation of various forms of digital masking, extending beyond traditional inpainting. We also provide a curated list of publicly available datasets that can support the development of new solutions, along with a selection of qualitative metrics for the robust evaluation of image inpainting algorithms.