Image Super Resolution On Urban100 2X

评估指标

PSNR

SSIM

评测结果

各个模型在此基准测试上的表现结果

			Paper Title	Repository
HMA†	35.24	0.9513	HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
DRCT-L	35.17	0.9516	DRCT: Saving Image Super-resolution away from Information Bottleneck
Hi-IR-L	35.16	0.9505	Hierarchical Information Flow for Generalized Efficient Image Restoration	-
HAT-L	35.09	0.9505	Activating More Pixels in Image Super-Resolution Transformer
HAT_FIR	34.94	-	SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
CPAT+	34.89	0.9487	Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution	-
HAT	34.81	0.9489	Activating More Pixels in Image Super-Resolution Transformer
CPAT	34.76	0.9481	Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution	-
SwinFIR	34.57	0.9473	SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
DRCT	34.54	0.9474	DRCT: Saving Image Super-resolution away from Information Bottleneck
DRLN+	33.54	0.9402	Densely Residual Laplacian Super-Resolution
HAN+	33.53	0.9398	Single Image Super-Resolution via a Holistic Attention Network
LTE	33.5	-	Local Texture Estimator for Implicit Representation Function
CSNLN	33.25	0.9386	Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
HBPN	33.12	0.938	Hierarchical Back Projection Network for Image Super-Resolution
ML-CrAIST	33.04	0.937	ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
ML-CrAIST-Li	32.93	0.9361	ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
DBPN-RES-MR64-3	32.92	0.935	Deep Back-Projection Networks for Single Image Super-resolution
FACD	32.878	-	Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution	-
SwinOIR	32.83	0.9353	Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy

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