Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features
This addresses a specific issue in image enhancement for computer vision applications, but it is incremental as it builds on existing CNN architectures.
The paper tackles the problem of checkerboard artifacts in image enhancement by proposing a novel CNN that uses fixed convolutional layers to prevent these artifacts, and it outperforms state-of-the-art methods with improvements in PSNR, SSIM, and NIQE metrics.
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.