Belangrijkste concepten
The author introduces a novel approach focusing on Learning Correction Errors (LCE) to address challenges in blind image super-resolution, utilizing a Corrector and Frequency-Self Attention block within an SR network.
Samenvatting
The content discusses a novel approach for blind image super-resolution by learning correction errors. It introduces a Corrector to obtain corrected low-resolution images and utilizes frequency learning within an SR network. The proposed method outperforms existing approaches in terms of visual quality and accuracy through extensive experiments across various settings.
Key points:
Previous methods rely on degradation estimation for blind image super-resolution.
Challenges with correction errors and degradation estimation are addressed.
A novel approach using Learning Correction Errors (LCE) is introduced.
The method employs a Corrector and Frequency-Self Attention block within an SR network.
Extensive experiments demonstrate the superiority of the proposed method in terms of visual quality and accuracy.
Statistieken
Previous methods [2,12,13,19–22,24,31,39,52] for blind SR problems often have two steps: 1): degradation estimation. 2): restoration using degradation
For scale 4 on isotropic kernel: PSNR - 32.14; SSIM - 0.8916
For scale 4 on anisotropic kernel: PSNR - 31.99; SSIM - 0.9233
Citaten
"Learning Correction Errors (LCE) focuses on addressing correction errors in blind image super-resolution."
"Our method outperforms existing approaches in terms of visual quality and accuracy."