Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather the paired training data by synthesizing LR images from HR images using a predetermined degradation model, e.g., Bicubic down-sampling. However, the realistic degradation type of test images may mismatch with the training-time degradation type due to the dynamic changes of the real-world scenarios, resulting in inferior-quality SR images. To address this, existing methods attempt to estimate the degradation model and train an image-specific model, which, however, is quite time-consuming and impracticable to handle rapidly changing domain shifts. Moreover, these methods largely concentrate on the estimation of one degradation type (e.g., blur degradation), overlooking other degradation types like noise and JPEG in real-world test-time scenarios, thus limiting their practicality. To tackle these problems, we present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types. Specifically, we design a second-order degradation scheme to construct paired data based on the degradation type of the test image, which is predicted by a pre-trained degradation classifier. Then, we adapt the SR model by implementing feature-level reconstruction learning from the initial test image to its second-order degraded counterparts, which helps the SR model generate plausible HR images. Extensive experiments are conducted on newly synthesized corrupted DIV2K datasets with 8 different degradations and several real-world datasets, demonstrating that our SRTTA framework achieves an impressive improvement over existing methods with satisfying speed. The source code is available at https://github.com/DengZeshuai/SRTTA.

我们提出了一种名为SRTTA的高效测试时自适应超分辨率框架，该框架能够快速将超分辨率模型适应于具有不同/未知退化类型的测试域。该框架通过预测经过预训练的退化分类器确定测试图像的退化类型，并设计了二阶退化方案构建基于测试图像退化类型的配对数据，然后通过从初始测试图像到其二阶退化版本的特征级重建学习来调整超分辨率模型，从而帮助模型生成合理的高分辨率图像。通过对几个实际数据集进行广泛实验，包括包含8种不同退化的合成DIV2K数据集，我们的SRTTA框架在速度和性能上都取得了显著的改进。源代码可在此https URL中获得。

高效测试时间自适应:二阶降质和重建的超分辨率