Previous approaches for blind image super-resolution (SR) have relied on degradation estimation to restore high-resolution (HR) images from their low-resolution (LR) counterparts. However, accurate degradation estimation poses significant challenges. The SR model's incompatibility with degradation estimation methods, particularly the Correction Filter, may significantly impair performance as a result of correction errors. In this paper, we introduce a novel blind SR approach that focuses on Learning Correction Errors (LCE). Our method employs a lightweight Corrector to obtain a corrected low-resolution (CLR) image. Subsequently, within an SR network, we jointly optimize SR performance by utilizing both the original LR image and the frequency learning of the CLR image. Additionally, we propose a new Frequency-Self Attention block (FSAB) that enhances the global information utilization ability of Transformer. This block integrates both self-attention and frequency spatial attention mechanisms. Extensive ablation and comparison experiments conducted across various settings demonstrate the superiority of our method in terms of visual quality and accuracy. Our approach effectively addresses the challenges associated with degradation estimation and correction errors, paving the way for more accurate blind image SR.

本文提出了一种针对失真图像盲超分辨率（SR）的学习误差校正（LCE）方法，该方法通过轻量级校正器获取校正后的低分辨率（CLR）图像，并在SR网络中联合优化SR性能，利用原始低分辨率图像和CLR图像的频率学习。同时，我们提出了一种新的频率自注意块（FSAB），它可以增强Transformer的全局信息利用能力，结合了自注意机制和频率空间注意机制。通过大量消融和对比实验在不同设置下的表现表明，我们的方法在视觉质量和准确性方面优于其他方法。我们的方法有效解决了与失真估计和校正误差相关的挑战，为更准确的失真图片超分辨率提供了一条途径。

通过频率自注意学习纠正错误的盲目图像超分辨率