Image inpainting with SVM-based scratch identification and fast Fourier convolution

Abstract

Old photographs often suffer from a variety of imperfections, such as scratches, stains, and fading. Various techniques have been proposed for image restoration, including image inpainting, denoising, deblurring, and super-resolution. In this paper, we propose a novel approach to restoring such images by leveraging deep learning techniques. Specifically, to accurately identify scratches in images, we train a classification network on a large dataset of paired old and repaired photos. Additionally, we employ a Fourier convolution-based neural network to repair the damaged areas of the images. Our results show that our approach outperforms existing methods in terms of both objective metrics and visual quality. We believe that our work has the potential to preserve valuable memories and historical artifacts for future generations.