论文标题
考虑到本地和全局功能，无形图像增强网络
Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features
论文作者
论文摘要
在本文中，我们提出了一个新型的卷积神经网络（CNN），该网络永远不会导致棋盘伪影以增强图像。在图像到图像翻译问题的研究领域中，众所周知，通常CNN产生的图像被棋盘伪影扭曲，这些棋盘伪影主要主要是在UPSMPLAPAGITATION上引起的。但是，从未讨论过图像增强中的棋盘伪影。在本文中，我们指出，将基于U-NET的CNN应用于图像增强作用会导致棋盘伪像。相比之下，包含固定卷积层的拟议网络可以完全防止工件。此外，提出的可以处理本地和全局功能的网络体系结构使我们能够提高图像增强的性能。实验结果表明，使用各种客观质量指标：PSNR，SSIM和NIQE，使用固定的卷积层可以防止棋盘伪影，而拟议的网络优于最先进的CNN图像增强方法。
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.
