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Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring

CVPR 2023 · Image generation › Image restoration · approach: Image deblurring · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use flow-based latent motion priors for non-uniform kernel estimation (ablation)KE-Net · GoProPSNR (reblur): 43.90 -> 44.56Helped
Add uncertainty learning to flow-prior kernel estimation (ablation)KE-Net · GoProPSNR (reblur): 44.56 -> 45.92Helped
Add flow-prior kernel estimation and uncertainty learning to NAFNet (ablation)NAFNet · GoPro, HIDE, RealBlur-R, RealBlur-JPSNR: 33.69 -> 34.06; 31.32 -> 31.74; 35.50 -> 36.25; 28.32 -> 29.87Helped
Use Whyte et al. kernel estimation instead of omitting kernel estimation (ablation)NAFNet · GoPro, HIDE, RealBlur-R, RealBlur-JPSNR: 33.69 -> 33.74; 31.32 -> 31.38; 35.50 -> 35.61; 28.32 -> 28.79Helped
Add the proposed kernel estimation module, including kernel estimation and multi-scale kernel attention (ablation)MIMO-UNet, MPRNet, and NAFNet · GoPro and HIDEPSNR: MIMO-UNet: 32.45 -> 32.83, 29.99 -> 30.16; MPRNet: 32.66 -> 33.04, 30.96 -> 31.13; NAFNet: 33.69 -> 34.06, 31.32 -> 31.7Helped
Use UFPNet instead of the strongest listed baselineBlind image deblurring models trained on GoPro · GoPro, HIDE, RealBlur-R, and RealBlur-JPSNR: 33.69 -> 34.06; 31.42 -> 31.74; 36.07 -> 36.25; 28.97 -> 29.87Helped
Use UFPNet instead of the strongest listed baselineModels trained and tested on RealBlur · RealBlur-R and RealBlur-JPSNR: 39.84 -> 40.61; 32.48 -> 33.35Helped

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