Abstract
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.
Abstract (translated)
先前的盲图像超分辨率（SR）方法依赖于降解估计来从低分辨率（LR）图像中恢复高分辨率（HR）图像。然而，准确的降解估计带来了显著的挑战。SR模型与降解估计方法的兼容性，特别是修复滤波器，可能导致修复误差。在本文中，我们引入了一种新的盲SR方法，专注于学习修复误差（LCE）。我们的方法采用轻量级的修复器来获得纠正后的低分辨率（CLR）图像。在SR网络中，我们通过同时利用原始LR图像和CLR图像的频率学习来共同优化SR性能。此外，我们提出了一种新的频率自注意块（FSAB），提高了Transformer的全局信息利用率能力。该 blocks 结合了自注意和频率空间关注机制。在各种设置的广泛消融和比较实验中进行的消融和比较实验证明了我们在视觉质量和准确性方面的优越性。我们的方法有效地解决了降解估计和修复误差带来的挑战，为更准确的盲图像SR铺平了道路。
URL
https://arxiv.org/abs/2403.07390
