Mask Optimization for Image Inpainting Using No-Reference Image Quality Assessment

Abstract

Image inpainting is a technique designed to remove unwanted regions from images and restore them. This technique is expected to be applied in various applications, including image editing, virtual reality (VR), mixed reality (MR), and augmented reality (AR). Typically, the inpainting process is based on missing regions predefined by user-applied masks. However, the specified areas may not always be ideal for inpainting, and the quality of the inpainting results varies depending on the annotated masked region. Therefore, this paper addresses the task of generating masks that improve inpainting results. To this end, we proposed a method that utilized No-Reference Image Quality Assessment (NR-IQA), which can score image quality without a reference image, to generate masked regions that maximize inpainting quality.