Ensemble based deep networks for image super-resolution

There have been significant advances in deep learning based single-image super-resolution (SISR) recently. With the advantage of deep neural networks, deep learning based methods can learn the mapping from low-resolution (LR) space to high-resolution (HR) space in an end-to-end manner. However, most of them only use a single model to generate HR result. This brings two drawbacks: (1) the risk of getting stuck in local optima and (2) the limited representational ability of single model when handling various input LR images. To overcome these problems, we novelly suggest a general way through introducing the idea of ensemble into SR task. Furthermore, instead of simple averaging, we propose a back-projection method to determine the weights of different models adaptively. In this paper, we focus on sparse coding network and propose ensemble based sparse coding network (ESCN). Through the combination of multiple models, our ESCN can generate more robust reconstructed results and achieve state-of-the-art performance.