The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to the absence of true degradation models in real-world scenarios, previous methods learn distinct representations by distinguishing different degradations in a batch. However, the most significant degradation differences may provide shortcuts for the learning of representations such that subtle difference may be discarded. In this paper, we propose an alternative to learn degradation representations through reproducing degraded low-resolution (LR) images. By guiding the degrader to reconstruct input LR images, full degradation information can be encoded into the representations. In addition, we develop a distribution alignment loss to facilitate the learning of the degradation representations by introducing a bounded constraint. Moreover, to achieve larger receptive fields to capture information from a wider region for better restoration results, we introduce a degradation-aware Mamba module to efficiently model long-range dependency between the anchor pixel and the surrounding informative pixels. And the module strikes a flexible adaption to various degradations based on the learned representations. Experiments show that our representations can extract accurate and highly robust degradation information. Evaluations on both synthetic and real images demonstrate that our ReDSR achieves state-of-the-art performance for the blind SR tasks.

翻译：图像超分辨率的性能在很大程度上依赖于退化信息的准确性，尤其是在盲设置下。由于现实场景中缺乏真实的退化模型，先前的方法通过区分批次中的不同退化来学习不同的表示。然而，最显著的退化差异可能为表示学习提供捷径，从而导致细微的差异被丢弃。在本文中，我们提出了一种通过重建退化的低分辨率图像来学习退化表示的替代方案。通过引导退化器重建输入的低分辨率图像，完整的退化信息可以被编码到表示中。此外，我们开发了一种分布对齐损失，通过引入有界约束来促进退化表示的学习。此外，为了获得更大的感受野以从更广的区域捕获信息，从而获得更好的恢复结果，我们引入了一个退化感知的Mamba模块，以高效地建模锚点像素与周围信息像素之间的长程依赖关系。该模块基于学习到的表示，能够灵活适应各种退化。实验表明，我们的表示能够提取准确且高度鲁棒的退化信息。在合成图像和真实图像上的评估均证明，我们的ReDSR在盲超分辨率任务中实现了最先进的性能。