HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
中文
HyperAI
HyperAI超神经
Toggle sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
首页
SOTA
图像超分辨率
Image Super Resolution On Urban100 2X
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
Columns
模型名称
PSNR
SSIM
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
0 of 29 row(s) selected.
Previous
Next