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Restormer: Efficient Transformer for High-Resolution Image Restoration

CVPR 2022 · Image generation › Image restoration · approach: Priors for restoration networks · 4,037 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Restormer instead of SPAIRImage deraining · Five-dataset averagePSNR: 1.05 dBHelped
Use Restormer instead of MPRNetSingle-image motion deblurring · GoPro, HIDE, RealBlur-R, RealBlur-JPSNR: 0.26 dBHelped
Use Restormer instead of IFANDefocus deblurring · DPDD combined scenesPSNR: ∼0.6 dBHelped
Replace the UNet Resblocks baseline with the full MDTA + GDFN Transformer block (ablation)Restormer · Urban100 σ=50PSNR: 29.11 -> 29.62Helped
Replace MTA with MDTA, adding channel-wise transposed attention (ablation)Restormer attention block · Urban100 σ=50PSNR: 29.28 -> 29.43Helped
Add gating to the feed-forward network (ablation)Restormer feed-forward network · Urban100 σ=50PSNR: +0.12 dBHelped
Add local depth-wise convolution to the feed-forward network (ablation)Restormer feed-forward network · Urban100 σ=50PSNR: 29.42 -> 29.54Helped
Add decoder level-1 concatenation (ablation)Restormer · Urban100 σ=50PSNR: 29.62 -> 29.66Helped
Add the refinement stage (ablation)Restormer · Urban100 σ=50PSNR: 29.66 -> 29.71Helped
Train on progressively larger patches rather than fixed 128² patches (ablation)Restormer · Urban100 σ=50PSNR: 29.71 -> 29.78Helped
Increase model dimension from 48 to 64 (ablation)Restormer · Urban100 σ=50PSNR: 29.71 -> 29.63Hurt

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