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 10761100 of 1589 papers

TitleStatusHype
Tarsier: Evolving Noise Injection in Super-Resolution GANsCode1
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results0
Residual Feature Distillation Network for Lightweight Image Super-ResolutionCode1
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesCode1
AdderSR: Towards Energy Efficient Image Super-Resolution0
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results0
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Interpretable Deep Multimodal Image Super-Resolution0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS0
Image Super-Resolution using Explicit Perceptual Loss0
MDCN: Multi-scale Dense Cross Network for Image Super-ResolutionCode1
Multi-Attention Based Ultra Lightweight Image Super-ResolutionCode1
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors0
Deep Variational Network Toward Blind Image RestorationCode1
Cascade Convolutional Neural Network for Image Super-Resolution0
PNEN: Pyramid Non-Local Enhanced Networks0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations0
E-FCNN for tiny facial expression recognition0
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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