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 13511375 of 1589 papers

TitleStatusHype
A Boosting Method to Face Image Super-resolution0
SPDER: Semiperiodic Damping-Enabled Object Representation0
Attention-based Image Upsampling0
Spectral Bayesian Uncertainty for Image Super-Resolution0
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution0
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution0
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation0
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation0
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging0
ATGV-Net: Accurate Depth Super-Resolution0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network0
SRFeat: Single Image Super-Resolution with Feature Discrimination0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network0
A Study in Dataset Pruning for Image Super-Resolution0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution0
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks0
A statistically constrained internal method for single image super-resolution0
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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