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 26–50 of 1589 papers

TitleStatusHype
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative ModelsCode3
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
CAMixerSR: Only Details Need More "Attention"Code3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
PP-MSVSR: Multi-Stage Video Super-ResolutionCode3
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual LearningCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Efficient and Explicit Modelling of Image Hierarchies for Image RestorationCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
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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