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 1–25 of 1589 papers

TitleStatusHype
Efficient Diffusion Model for Image Restoration by Residual ShiftingCode5
MambaIR: A Simple Baseline for Image Restoration with State-Space ModelCode5
Arbitrary-steps Image Super-resolution via Diffusion InversionCode5
GLEAN: Generative Latent Bank for Image Super-Resolution and BeyondCode5
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image RestorationCode4
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
A Survey on Visual MambaCode4
Adversarial Diffusion Compression for Real-World Image Super-ResolutionCode4
One-Step Effective Diffusion Network for Real-World Image Super-ResolutionCode4
NAFSSR: Stereo Image Super-Resolution Using NAFNetCode4
Diffusion Models: A Comprehensive Survey of Methods and ApplicationsCode4
DiffBIR: Towards Blind Image Restoration with Generative Diffusion PriorCode4
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA ApproachCode4
SeeSR: Towards Semantics-Aware Real-World Image Super-ResolutionCode4
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space ModelCode4
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
CAMixerSR: Only Details Need More "Attention"Code3
PP-MSVSR: Multi-Stage Video Super-ResolutionCode3
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
Degradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCode3
Activating More Pixels in Image Super-Resolution TransformerCode3
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Benchmark Results

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