  The deep learning technique was used to increase the performance of single
image super-resolution (SISR). However, most existing CNN-based SISR approaches
primarily focus on establishing deeper or larger networks to extract more
significant high-level features. Usually, the pixel-level loss between the
target high-resolution image and the estimated image is used, but the neighbor
relations between pixels in the image are seldom used. On the other hand,
according to observations, a pixel's neighbor relationship contains rich
information about the spatial structure, local context, and structural
knowledge. Based on this fact, in this paper, we utilize pixel's neighbor
relationships in a different perspective, and we propose the differences of
neighboring pixels to regularize the CNN by constructing a graph from the
estimated image and the ground-truth image. The proposed method outperforms the
state-of-the-art methods in terms of quantitative and qualitative evaluation of
the benchmark datasets.
  Keywords: Super-resolution, Convolutional Neural Networks, Deep Learning
