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SOTA
Image Super-Resolution
Image Super Resolution On Set14 4X Upscaling
Image Super Resolution On Set14 4X Upscaling
Metrics
PSNR
SSIM
Results
Performance results of various models on this benchmark
Columns
Model Name
PSNR
SSIM
Paper Title
Repository
ATD
29.24
0.7974
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
-
Manifold Simplification
28.80
0.7856
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
-
BSRN
28.56
0.7803
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
-
HBPN
28.67
0.785
Hierarchical Back Projection Network for Image Super-Resolution
-
Extracter-rec
28.09
0.782
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution
-
HMA†
29.51
0.8019
HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
-
ENet-E
28.42
0.7774
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
-
SPSR
26.64
0.7930
Structure-Preserving Super Resolution with Gradient Guidance
-
MaIR
29.2
0.7958
MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
-
ProSR
28.94
-
A Fully Progressive Approach to Single-Image Super-Resolution
-
CRAFT
28.85
0.7872
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
-
SESR
28.32
0.784
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
-
AESOP
27.421
0.7438
Auto-Encoded Supervision for Perceptual Image Super-Resolution
-
GMFN
28.84
0.7888
Gated Multiple Feedback Network for Image Super-Resolution
-
CPAT
29.34
0.7991
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution
-
bicubic
-
0.7486
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
-
SPMC
27.57
0.76
Detail-revealing Deep Video Super-resolution
-
4PP-EUSR
27.6222
0.7419
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
-
Edge-informed SR
25.19
0.894
Edge-Informed Single Image Super-Resolution
-
S-RFN
-
0.7946
Progressive Perception-Oriented Network for Single Image Super-Resolution
-
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Image Super Resolution On Set14 4X Upscaling | SOTA | HyperAI