CG-GAN: Class-Attribute Guided Generative Adversarial Network for Old Photo Restoration

Old photos are an important carrier to preserve the past. Usually, the degradation of old photos is rather diverse and complex. Therefore, the existing methods to solve conventional restoration tasks are difficult to generalize. To solve this problem, we propose a novel method based on generative adversarial network. Our method utilizes the class-attributes of old photos to complete restoration in latent space. Specifically, we divide the process of restoring old photos into two stages, one is global defect restoration stage and the other is local detail restoration stage. In global defect restoration stage, we extract the latent representations of four classes of high-level attributes that are smoothness, clarity, connectivity and completeness. We use latent class-attribute information to restore global defects in latent space and we obtain conditional control vector through a condition network to guide the subsequent local detail restoration stage. In local detail restoration stage, we propose a dynamic condition-guided restoration module that selects the most suitable combination of features to further restore local details through a dynamic network. In addition, we propose a dual discriminator to pay more attention to style and defect restoration. We ignore the complex degradation of old photos to directly restore advanced class-attributes. Therefore, our method has better generalization performance. Experiments show that our method is superior to other existing methods of image restoration in terms of visual quality and numerical metrics.