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

模型名称	PSNR	SSIM	Paper Title	Repository
SwinOIR	32.83	0.9353	Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy	-
HMA†	35.24	0.9513	HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution	-
SPBP-L+	32.07	0.9277	Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution	-
FACD	32.878	-	Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution	-
CSNLN	33.25	0.9386	Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining	-
MWCNN	32.3	-	Multi-level Wavelet-CNN for Image Restoration	-
VDSR [[Kim et al.2016a]]	30.76	-	Accurate Image Super-Resolution Using Very Deep Convolutional Networks	-
HAT	34.81	0.9489	Activating More Pixels in Image Super-Resolution Transformer	-
DRCT-L	35.17	0.9516	DRCT: Saving Image Super-resolution away from Information Bottleneck	-
DBPN-RES-MR64-3	32.92	0.935	Deep Back-Projection Networks for Single Image Super-resolution	-
PMRN+	32.78	0.9342	Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution	-
DRLN+	33.54	0.9402	Densely Residual Laplacian Super-Resolution	-
HAT-L	35.09	0.9505	Activating More Pixels in Image Super-Resolution 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	-
DRCN [[Kim et al.2016b]]	30.75	-	Deeply-Recursive Convolutional Network for Image Super-Resolution	-
DnCNN-3	30.74	-	Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising	-
SwinFIR	34.57	0.9473	SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution	-
Hi-IR-L	35.16	0.9505	Hierarchical Information Flow for Generalized Efficient Image Restoration	-
ML-CrAIST	33.04	0.937	ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer	-
DRCT	34.54	0.9474	DRCT: Saving Image Super-resolution away from Information Bottleneck	-

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