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Unfolding the Alternating Optimization for Blind Super Resolution

NeurIPS 2020 · Image generation › Super-resolution · approach: Blind super-resolution · 146 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use end-to-end alternating Estimator–Restorer optimization instead of the two-step KernelGAN + ZSSR pipelineBlind SR · DIV2KRK ×2PSNR: 30.36 -> 32.56Helped
use end-to-end alternating Estimator–Restorer optimization instead of the two-step KernelGAN + ZSSR pipelineBlind SR · DIV2KRK ×4PSNR: 26.81 -> 27.55Helped
replace the two-step IKC solution with DANBlind SR · Manga109 Gaussian8 ×3PSNR: 28.21 -> 33.16Helped
replace the two-step IKC solution with DANBlind SR · Set5, Set14, BSD100, Urban100, Manga109 Gaussian8 ×2–×4PSNRHelped
replace estimated kernels with ground-truth kernels for the Restorer (ablation)DAN · Set5, Set14, BSD100, Urban100, Manga109 Gaussian8 ×4PSNR: 31.89 -> 31.85; 28.43 -> 28.42; 27.51 -> 27.51; 25.86 -> 25.87; 30.50 -> 30.51No clear effect
test DAN with more iterations than its four-iteration training configuration (ablation)DAN · Set14PSNR: 20.42dB -> 20.43dBNo clear effect
use DAN instead of KernelGAN + ZSSRBlind SR · Gaussian8 images from Set5inference time per image: 415.7 seconds -> 0.75 secondsHelped
use DAN instead of IKCBlind SR · Gaussian8 images from Set5inference time per image: 3.93 seconds -> 0.75 secondsHelped

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