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 301325 of 1589 papers

TitleStatusHype
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion ModelsCode1
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
Adaptive Convolutional Neural Network for Image Super-resolutionCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Deep Burst Super-ResolutionCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
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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