Learning Correction Errors via Frequency-Self Attention for Blind Image
  Super-Resolution
Haochen Sun Yan Yuan Lijuan Su Haotian Shao
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.