Real Image Restoration Using VAEs

Abstract

Abstract— Old photos are an integral part of everybody’s life; they remind us of how one person has spent their life. As people used hard copies of photos before, those photos suffered severe degradation. This degradation in real-time images is intricate, causing thetypical restoration that might be solved through supervised learning to fail to generalize due to the domain gap between synthetic and real images. Therefore, this method uses various autoencoders to restore and colourize old images. Furthermore, this model uses a unique triplet domain translation network on real images and synthetic photo pairs. Precisely, VAEs, which are variational autoencoders, are trained to transform old pictures and clean pictures into two latent spaces. Therefore, the translation between these two latent spaces is comprehended with simulated paired data. This translation generalizes well to authentic images because the domain gap is encompassed in the close-packed latent space. Moreover, to manoeuvre numerous degradations present in one old picture, this model designs aworld branchwith a partial non-local block targeting the structured faults, like scrapes and dirt marks, and an area branch targeting unstructured faults, like noisesand fuzziness. Two branches are blended within the latent space, resulting in an improved ability to renew old pictures from numerous defects. Additionally, it applies another face refinementnetwork to revive fine details of faces within the old pictures, thus generating photos with amplified quality. Another autoencoder is encoded with colour images, and then the decoder decodes the features extracted from the encoder. Once a model is trained, testing is performed to colourize the photographs. Keywords— Degraded pictures, Variational Auto Encoders, Domain gap, Triplet domaintranslation.