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 501525 of 1589 papers

TitleStatusHype
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
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
Gated Fusion Network for Degraded Image Super ResolutionCode1
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
Unpaired Image Super-Resolution using Pseudo-SupervisionCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial NetworkCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
Scale-wise Convolution for Image RestorationCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
Single Image Super-Resolution via CNN Architectures and TV-TV MinimizationCode1
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyCode1
Towards Real Scene Super-Resolution with Raw ImagesCode1
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Meta-SR: A Magnification-Arbitrary Network for Super-ResolutionCode1
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
Show:102550
← PrevPage 21 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