Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details. However, it is notorious that GAN-based SR models will inevitably produce unpleasant and undesirable artifacts, especially in practical scenarios. Previous works typically suppress artifacts with an extra loss penalty in the training phase. They only work for in-distribution artifact types generated during training. When applied in real-world scenarios, we observe that those improved methods still generate obviously annoying artifacts during inference. In this paper, we analyze the cause and characteristics of the GAN artifacts produced in unseen test data without ground-truths. We then develop a novel method, namely, DeSRA, to Detect and then Delete those SR Artifacts in practice. Specifically, we propose to measure a relative local variance distance from MSE-SR results and GAN-SR results, and locate the problematic areas based on the above distance and semantic-aware thresholds. After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data. Equipped with our DeSRA, we can successfully eliminate artifacts from inference and improve the ability of SR models to be applied in real-world scenarios. The code will be available at https://github.com/TencentARC/DeSRA.

翻译：图像超分辨率（SR）结合生成对抗网络（GAN）在恢复真实细节方面取得了巨大成功。然而，众所周知，基于GAN的SR模型在实际场景中不可避免地会产生令人不悦且不期望的伪影。以往的工作通常通过在训练阶段添加额外的损失惩罚来抑制伪影，但这些方法仅适用于训练过程中产生的分布内伪影类型。在实际应用中，我们观察到这些改进方法在推理时仍会产生明显恼人的伪影。本文分析了无真实标签的未见测试数据中GAN伪影的成因及特征，并提出了一种名为DeSRA的新方法，用于在实践中检测并消除这些SR伪影。具体而言，我们提出从基于MSE的SR结果与基于GAN的SR结果中测量相对局部距离，并基于该距离及语义感知阈值定位问题区域。在检测到伪影区域后，我们开发了一种微调流程，通过少量样本改进基于GAN的SR模型，使其能够处理更多未见真实数据中的同类伪影。借助DeSRA，我们成功消除了推理过程中的伪影，并提升了SR模型在真实场景中的应用能力。代码将发布于https://github.com/TencentARC/DeSRA。