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 11761200 of 1589 papers

TitleStatusHype
Robust Single-Image Super-Resolution via CNNs and TV-TV MinimizationCode1
Unsupervised Real-world Image Super Resolution via Domain-distance Aware TrainingCode1
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New StrategyCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Structure-Preserving Super Resolution with Gradient GuidanceCode1
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey0
Learning regularization and intensity-gradient-based fidelity for single image super resolution0
Deep Unfolding Network for Image Super-ResolutionCode1
Across-scale Process Similarity based Interpolation for Image Super-Resolution0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention0
High-Resolution Daytime Translation Without Domain LabelsCode0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks0
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Hierarchical Neural Architecture Search for Single Image Super-ResolutionCode1
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative ModelsCode3
Perceptual Image Super-Resolution with Progressive Adversarial Network0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model0
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
Turbulence Enrichment using Physics-informed Generative Adversarial Networks0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better0
Gated Fusion Network for Degraded Image Super ResolutionCode1
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data0
Unpaired Image Super-Resolution using Pseudo-SupervisionCode1
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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
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
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