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Zero-Shot Dual-Lens Super-Resolution

CVPR 2023 · Image generation › Super-resolution · approach: Real-world super-resolution · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Adding degradation-invariant alignment losses (spatial adversarial, frequency adversarial, feature contrastive) to L2 alignment and degradation-aware training (ablation)ZeDuSR · HCI new 2x IGPSNR: 30.48 to 31.01 dBHelped
Degradation-aware training strategy (DaTS) for patch cropping (ablation)ZeDuSR · HCI new 2x IGPSNR: 30.89 to 31.01 dBHelped
Initialize SR network from bicubic-pretrained model (ZeDuSR*)ZeDuSR · HCI new 2x IGPSNR: 31.01 to 31.17 dBHelped
Swap alignment backbone (FlowNet-S to SPyNet or PWCNet) (ablation)ZeDuSR · HCI new 2x IG/AGPSNR: IG 31.05 (SPyNet), 29.98 (PWCNet) vs 31.01; AG 29.97 and 30.06 vs 30.02No clear effect
Swap SR backbone (RCAN to CSNLN or SwinIR) within ZeDuSR (ablation)ZeDuSR · HCI new 2x IGPSNR: 31.03 (CSNLN), 31.04 (SwinIR) vs 31.01 (RCAN)No clear effect
Dual-lens zero-shot ZeDuSR vs. dual-lens/RefSR/SISR baselinesZeDuSR · Synthesized IG/AG 2x and 4x (Table 1)PSNR: superior in most casesHelped
Blind SISR (DAN, DCLS) vs ZeDuSR on AG-kernel-pool trainingZeDuSR · HCI new and Middlebury2021 (Table 2)PSNR: DCLS 30.63 beats ZeDuSR 31.01 on HCI new IG SSIM 0.8651 vs 0.8529; ZeDuSR better overallMixed

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