Image Inpainting as a Quadratic Programming Task

Digital image inpainting is an image restoration technique used to replace missing, damaged, or deteriorated parts of images. Classical textual and structural, as well as deep neural network learning-based, methods have been proposed for inpainting digital images originating from various sources. In this paper we propose a method that falls within the realm of classical methods. The inpainting task is formulated as a quadratic programming problem, and two solution methods, unsupervised and supervised, are described. The proposed methods are experimentally evaluated on the different classes of images. We analyze and discuss the results and identify possible future research directions.