Lossy compression will inevitably introduce image artifacts in the decoded image and degrade the image quality. In recent years, convolutional neural network (CNN) has been exploited for removing compression artifacts with great success. However, most existing CNN-based methods only utilize image's local prior without considering the global prior on the training of their networks. In this letter, a novel CNN, called the local and global priors network (LGPNet), is proposed that simultaneously learns both the local and the global priors for removing compression image artifacts. To achieve this goal, a dual-attention unit (DAU) is developed and incorporated into the well-known U-Net architecture for learning a better local prior. Meanwhile, the global prior is also learned from the entire image via our proposed global prior network. Extensive experimental results have clearly demonstrated that our proposed LGPNet is able to effectively remove image artifacts and greatly improve the image quality of JPEG-compressed images.

中文翻译：

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学习 JPEG 图像伪影去除的局部和全局先验

有损压缩不可避免地会在解码图像中引入图像伪影并降低图像质量。近年来，卷积神经网络 (CNN) 已被用于去除压缩伪影，并取得了巨大成功。然而，大多数现有的基于 CNN 的方法只利用图像的局部先验，而不考虑其网络训练的全局先验。在这封信中，提出了一种称为局部和全局先验网络 (LGPNet) 的新型 CNN，它同时学习局部和全局先验以去除压缩图像伪影。为了实现这一目标，开发了一个双注意单元 (DAU) 并将其合并到著名的 U-Net 架构中，以学习更好的局部先验。同时，全局先验也是通过我们提出的全局先验网络从整个图像中学习的。