7 research outputs found
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
Recently, many convolutional neural networks for single image
super-resolution (SISR) have been proposed, which focus on reconstructing the
high-resolution images in terms of objective distortion measures. However, the
networks trained with objective loss functions generally fail to reconstruct
the realistic fine textures and details that are essential for better
perceptual quality. Recovering the realistic details remains a challenging
problem, and only a few works have been proposed which aim at increasing the
perceptual quality by generating enhanced textures. However, the generated fake
details often make undesirable artifacts and the overall image looks somewhat
unnatural. Therefore, in this paper, we present a new approach to
reconstructing realistic super-resolved images with high perceptual quality,
while maintaining the naturalness of the result. In particular, we focus on the
domain prior properties of SISR problem. Specifically, we define the
naturalness prior in the low-level domain and constrain the output image in the
natural manifold, which eventually generates more natural and realistic images.
Our results show better naturalness compared to the recent super-resolution
algorithms including perception-oriented ones.Comment: Presented in CVPR 201