  In this paper, we propose a novel convolutional neural network (CNN) that
never causes checkerboard artifacts, for image enhancement. In research fields
of image-to-image translation problems, it is well-known that images generated
by usual CNNs are distorted by checkerboard artifacts which mainly caused in
forward-propagation of upsampling layers. However, checkerboard artifacts in
image enhancement have never been discussed. In this paper, we point out that
applying U-Net based CNNs to image enhancement causes checkerboard artifacts.
In contrast, the proposed network that contains fixed convolutional layers can
perfectly prevent the artifacts. In addition, the proposed network
architecture, which can handle both local and global features, enables us to
improve the performance of image enhancement. Experimental results show that
the use of fixed convolutional layers can prevent checkerboard artifacts and
the proposed network outperforms state-of-the-art CNN-based image-enhancement
methods in terms of various objective quality metrics: PSNR, SSIM, and NIQE.
