HyperAI超神経

Image Super Resolution On Urban100 2X

評価指標

PSNR
SSIM

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
PSNR
SSIM
Paper TitleRepository
SwinOIR32.830.9353Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
HMA†35.240.9513HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
SPBP-L+32.070.9277Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution
FACD32.878-Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution-
CSNLN33.250.9386Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
MWCNN32.3-Multi-level Wavelet-CNN for Image Restoration
VDSR [[Kim et al.2016a]]30.76-Accurate Image Super-Resolution Using Very Deep Convolutional Networks
HAT34.810.9489Activating More Pixels in Image Super-Resolution Transformer
DRCT-L35.170.9516DRCT: Saving Image Super-resolution away from Information Bottleneck
DBPN-RES-MR64-332.920.935Deep Back-Projection Networks for Single Image Super-resolution
PMRN+32.780.9342Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution-
DRLN+33.540.9402Densely Residual Laplacian Super-Resolution
HAT-L35.090.9505Activating More Pixels in Image Super-Resolution Transformer
ML-CrAIST-Li32.930.9361ML-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-330.74-Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
SwinFIR34.570.9473SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
Hi-IR-L35.160.9505Hierarchical Information Flow for Generalized Efficient Image Restoration-
ML-CrAIST33.040.937ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
DRCT34.540.9474DRCT: Saving Image Super-resolution away from Information Bottleneck
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