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

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Showing 1051–1100 of 1589 papers

TitleStatusHype
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning—0
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results—0
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results—0
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results—0
Progressive Generative Adversarial Networks for Medical Image Super resolution—0
Progressively Unfreezing Perceptual GAN—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Proximal Splitting Networks for Image Restoration—0
PTSR: Patch Translator for Image Super-Resolution—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
QMambaBSR: Burst Image Super-Resolution with Query State Space Model—0
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Quantum Annealing for Single Image Super-Resolution—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
RAISR: Rapid and Accurate Image Super Resolution—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Real-World Single Image Super-Resolution Under Rainy Condition—0
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Image restoration quality assessment based on regional differential information entropy—0
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art—0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Residual Feature Aggregation Network for Image Super-Resolution—0
Residual Networks for Light Field Image Super-Resolution—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Rethinking Image Evaluation in Super-Resolution—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
RGB-Guided Resolution Enhancement of IR Images—0
Robust Regression via Deep Negative Correlation Learning—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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