# Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts

arXiv: https://arxiv.org/abs/1806.02658 · HTML: https://mlsanity.com/paper/1806.02658/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

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- [A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain](https://mlsanity.com/paper/2108.01892/) (2021)
- [On Unsupervised Image-to-image translation and GAN stability](https://mlsanity.com/paper/2403.09646/) (2023)

## Abstract (arXiv)

It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical SR methods to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts under two loss conditions: mean square error and perceptual loss, while keeping excellent properties that the SR methods have.