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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 1589 papers

TitleStatusHype
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Progressively Unfreezing Perceptual GAN—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
Residual Feature Aggregation Network for Image Super-Resolution—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results—0
Neural Differential Equations for Single Image Super-resolution—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Time accelerated image super-resolution using shallow residual feature representative network—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

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
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—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