There are multiple factors that can contribute to the degradation of an image. The process of recovering such images to their initial state is called Image Restoration. Nowadays many deep learning techniques have been proposed that claim to solve this problem. In this work, I select a few deep learning models both single (focus only on one type of degradation, such as super-resolution methods) and mixed degradation (when tackling all the defects at the same time) achieving state-of-the-art performance on different restoration tasks (Deblurring, Denoising, Super-Resolution, etc.), test them on a synthetically degraded dataset and evaluate them according to two objective metrics (PSNR and SSIM) as well as subjectively, through human perception. These are then combined and compared with the state-of-the-art method in old photo restoration which comprises an image-to-image translation framework based on deep latent space translation. This state-of-the-art approach outperformed all other methods and combinations of by a large margin.