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Image Super Resolution On Urban100 3X

Metriken

PSNR
SSIM

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper TitleRepository
Hi-IR-L31.070.902Hierarchical Information Flow for Generalized Efficient Image Restoration-
HMA†31.000.8984HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
HAT-L30.920.8981Activating More Pixels in Image Super-Resolution Transformer
HAT_FIR30.77-SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
HAT30.700.8949Activating More Pixels in Image Super-Resolution Transformer
CPAT+30.630.8934Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
CPAT30.520.8923Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
SwinFIR30.430.8913SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
IPT29.49-Pre-Trained Image Processing Transformer
LTE29.41-Local Texture Estimator for Implicit Representation Function
DRLN+29.370.8746Densely Residual Laplacian Super-Resolution
HAN+29.210.8710Single Image Super-Resolution via a Holistic Attention Network
CSNLN29.130.8712Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
ML-CrAIST28.890.8676ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
SwinOIR28.870.8674Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
FACD28.818-Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution-
ML-CrAIST-Li28.730.8651ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
SRFBN28.73-Feedback Network for Image Super-Resolution
IMDN28.17-Lightweight Image Super-Resolution with Information Multi-distillation Network
MWCNN28.13-Multi-level Wavelet-CNN for Image Restoration
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