Joint Denoising and Super-Resolution via Generative Adversarial Training

Single image denoising and super-resolution are sitting in the core of various image processing and pattern recognition applications. Typically, these two tasks are handled separately, without regarding to joint reinforcement and learning. The former deals with equal-size pixel-to-pixel translation, while the latter deals with scaling up amount of input pixels. In this paper, we propose a Generative Adversarial Network(GAN) towards joint learning of single image denoising and super-resolution. In principle, our design allows both tasks to share several common building blocks, with the linking between both outputs to reinforce each other. Such a reinforcement is accomplished via designing a novel generative network through optimizing a novel loss function to achieve both denoising and super-resolution. Quantitatively comparing to a set of alternative approaches and baselines, the experiment demonstrated superior performance our method in denoising and super-resolution with high upscaling factors.