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Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-Based Super-resolution

AAAI 2022 · Image generation › Super-resolution · approach: Real-world super-resolution · 32 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use AMSA-rec instead of C²-Matching-recReference-based super-resolution · CUFED5PSNR: 28.24 -> 28.50Helped
Use AMSA-rec instead of C²-Matching-recReference-based super-resolution · Sun80PSNR: 30.18 -> 30.29Helped
Use AMSA-rec instead of MASA-recReference-based super-resolution · Urban100PSNR: 26.09 -> 26.18Helped
Use AMSA-rec instead of C²-Matching-recReference-based super-resolution · Manga109PSNR: 30.47 -> 30.57Helped
Add MSDA (ablation)AMSA with CFE-PatchMatch · CUFED5PSNR: 28.21 -> 28.50Helped
Replace CFE-PatchMatch with enumerated matching (ablation)AMSA with MSDA · CUFED5PSNR: 28.50 -> 28.51No clear effect
Replace CFE-PatchMatch with enumerated matching (ablation)AMSA with MSDA · CUFED5GFLOPs: 279.52 -> 29297.82Hurt
Replace CFE-PatchMatch with MEM (ablation)AMSA with MSDA · CUFED5PSNR: 28.43 -> 28.50Hurt
Replace CFE-PatchMatch with MEM (ablation)AMSA with MSDA · CUFED5GFLOPs: 1479.87 -> 279.52Hurt
Apply matching iterations coarse-to-fine (ablation)Embedded PatchMatch · CUFED5 matching convergenceMSEHelped
Use Dynamic Aggregation (ablation)MSDA · CUFED5 with minor scale misalignmentsuper-resolution performanceHelped
Use Multi-Scale Aggregation (ablation)MSDA · CUFED5 with large scale misalignmentsuper-resolution performanceHelped

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