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홈플랫폼문서뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Image Super Resolution On Set14 3X Upscaling

평가 지표

PSNR
SSIM

평가 결과

이 벤치마크에서 각 모델의 성능 결과

Paper TitleRepository
Hi-IR-L31.550.8616Hierarchical Information Flow for Generalized Efficient Image Restoration-
HMA†31.470.8585HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
HAT-L31.470.8584Activating More Pixels in Image Super-Resolution Transformer
HAT_FIR31.37-SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
HAT31.330.8576Activating More Pixels in Image Super-Resolution Transformer
SwinFIR31.240.8566SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
CPAT+31.190.8559Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
CPAT31.150.8557Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
IPT30.85-Pre-Trained Image Processing Transformer
LTE30.8-Local Texture Estimator for Implicit Representation Function
DRLN+30.80.8498Densely Residual Laplacian Super-Resolution
HAN+30.790.8487Single Image Super-Resolution via a Holistic Attention Network
CSNLN30.660.8482Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
SwinOIR30.650.8493Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
ML-CrAIST30.390.8488ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
IMDN30.32-Lightweight Image Super-Resolution with Information Multi-distillation Network
ML-CrAIST-Li30.230.8474ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
MWCNN30.16-Multi-level Wavelet-CNN for Image Restoration
SRFBN30.1-Feedback Network for Image Super-Resolution
LCSCNet29.87-LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
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