  Implicit neural representation, which expresses an image as a continuous
function rather than a discrete grid form, is widely used for image processing.
Despite its outperforming results, there are still remaining limitations on
restoring clear shapes of a given signal such as the edges of an image. In this
paper, we propose Gradient Magnitude Adjustment algorithm which calculates the
gradient of an image for training the implicit representation. In addition, we
propose Edge-oriented Representation Network (EoREN) that can reconstruct the
image with clear edges by fitting gradient information (Edge-oriented module).
Furthermore, we add Channel-tuning module to adjust the distribution of given
signals so that it solves a chronic problem of fitting gradients. By separating
backpropagation paths of the two modules, EoREN can learn true color of the
image without hindering the role for gradients. We qualitatively show that our
model can reconstruct complex signals and demonstrate general reconstruction
ability of our model with quantitative results.
