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 12511275 of 1589 papers

TitleStatusHype
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration0
Training a Task-Specific Image Reconstruction Loss0
Training Set Effect on Super Resolution for Automated Target Recognition0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images0
Transformers in Vision: A Survey0
Transport-based analysis, modeling, and learning from signal and data distributions0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution0
Triple Attention Mixed Link Network for Single Image Super Resolution0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution0
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance0
Uncertainty-Driven Loss for Single Image Super-Resolution0
Uncertainty Estimation for Super-Resolution using ESRGAN0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration0
Show:102550
← PrevPage 51 of 64Next →

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