  Several methods have recently been proposed for the Single Image
Super-Resolution (SISR) problem. The current methods assume that a single
low-resolution image can only yield a single high-resolution image. In
addition, all of these methods use low-resolution images that were artificially
generated through simple bilinear down-sampling. We argue that, first and
foremost, the problem of SISR is an one-to-many mapping problem between the
low-resolution and all possible candidate high-resolution images and we address
the challenging task of learning how to realistically degrade and down-sample
high-resolution images. To circumvent this problem, we propose SR-NAM which
utilizes the Non-Adversarial Mapping (NAM) technique. Furthermore, we propose a
degradation model that learns how to transform high-resolution images to
low-resolution images that resemble realistically taken low-resolution photos.
Finally, some qualitative results for the proposed method along with the
weaknesses of SR-NAM are included.
