Abstract: Real-world Super-Resolution (SR) is a very challenging task to reconstruct a higher resolution image from a real-world image. Most of the high performance SR methods rely on availability of LR-HR paired datasets, target domain images, or degradation priors. These information, however, are usually not available in real world use, thus these methods are not often practical for real world obtained images. Recent studies related to real-world SR mainly focus on constructing the LR-HR paired dataset. The methods estimate the noises and the blur kernels from real-world images to generate a new training set. However, these methods use the degradation only for dataset construction. In this paper, we propose a novel real-world SR method called Deep Degradation Prior-based SR (DDP-SR). Upon completion of training, denoising network and kernel estimation network within DDP-SR becomes capable of extracting degradation representation of any given input image. Thus, the model works regardless of whether the input image from the same or the different domain of the training set. As such, DDP-SR generalizes well on images from different domain while it also outperforms the state-of-the-art methods in the SR task.