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 51–75 of 1589 papers

TitleStatusHype
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual LearningCode2
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
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
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-ResolutionCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
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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