SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Showing 101125 of 1589 papers

TitleStatusHype
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-ResolutionCode2
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
A Spectral Diffusion Prior for Hyperspectral Image Super-ResolutionCode1
Deep Burst Super-ResolutionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
Activating Wider Areas in Image Super-ResolutionCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
Show:102550
← PrevPage 5 of 64Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified