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
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature TransformCode3
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
PP-MSVSR: Multi-Stage Video Super-ResolutionCode3
Activating More Pixels in Image Super-Resolution TransformerCode3
CAMixerSR: Only Details Need More "Attention"Code3
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative ModelsCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge SurveyCode3
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
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Efficient and Explicit Modelling of Image Hierarchies for Image RestorationCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
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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