HyperAIHyperAI超神経
ホームプラットフォームドキュメントニュース論文チュートリアルデータセット百科事典SOTALLMモデルGPU ランキング学会
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Image Super Resolution On Set5 2X Upscaling

評価指標

PSNR
SSIM

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

Paper TitleRepository
DRCT-L39.140.9658DRCT: Saving Image Super-resolution away from Information Bottleneck
HMA†38.950.9649HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
HAT-L38.910.9646Activating More Pixels in Image Super-Resolution Transformer
Hi-IR-L38.870.9663Hierarchical Information Flow for Generalized Efficient Image Restoration-
HAT_FIR38.74-SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
HAT38.730.9637Activating More Pixels in Image Super-Resolution Transformer
CPAT+38.720.9635Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
DRCT38.720.9646DRCT: Saving Image Super-resolution away from Information Bottleneck
CPAT38.680.9633Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
SwinFIR38.650.9633SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
EDT-B38.630.9632On Efficient Transformer-Based Image Pre-training for Low-Level Vision
MaIR+38.620.963MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
MaIR38.560.9628MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
DRLN+38.340.9619Densely Residual Laplacian Super-Resolution
LTE38.33-Local Texture Estimator for Implicit Representation Function
HAN+38.330.9299Single Image Super-Resolution via a Holistic Attention Network
CSNLN38.280.9616Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
FACD38.242-Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution-
PMRN+38.220.9612Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution-
SwinOIR38.210.9614Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
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