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Image Super Resolution On Set5 3X Upscaling

Métriques

PSNR

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper TitleRepository
HMA†35.35HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
DRCT-L35.32DRCT: Saving Image Super-resolution away from Information Bottleneck
HAT-L35.28Activating More Pixels in Image Super-Resolution Transformer
HAT_FIR35.21SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
Hi-IR-L35.2Hierarchical Information Flow for Generalized Efficient Image Restoration-
CPAT+35.19Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
DRCT35.18DRCT: Saving Image Super-resolution away from Information Bottleneck
HAT35.16Activating More Pixels in Image Super-Resolution Transformer
CPAT35.16Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution-
SwinFIR35.15SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
EDT-B35.13On Efficient Transformer-Based Image Pre-training for Low-Level Vision
LTE34.89Local Texture Estimator for Implicit Representation Function
DRLN+34.86Densely Residual Laplacian Super-Resolution
HAN+34.85Single Image Super-Resolution via a Holistic Attention Network
CSNLN34.74Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
FACD34.729Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution-
SRFBN34.70Feedback Network for Image Super-Resolution
ML-CrAIST34.7ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
SwinOIR34.69Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
PMRN+34.65Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution-
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