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 2650 of 1589 papers

TitleStatusHype
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature TransformCode3
SwinIR: Image Restoration Using Swin TransformerCode3
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
PP-MSVSR: Multi-Stage Video Super-ResolutionCode3
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
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
Effective Diffusion Transformer Architecture for 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
Efficient and Explicit Modelling of Image Hierarchies for Image RestorationCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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