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 12011225 of 1589 papers

TitleStatusHype
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
Facial Attribute Capsules for Noise Face Super Resolution0
Generator From Edges: Reconstruction of Facial Images0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries0
Learning Deep Analysis Dictionaries for Image Super-Resolution0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization0
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial NetworkCode1
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques0
Multimodal Deep Unfolding for Guided Image Super-Resolution0
Learned Multi-View Texture Super-Resolution0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution0
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES0
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
Characteristic Regularisation for Super-Resolving Face Images0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network0
Exploiting Style and Attention in Real-World Super-Resolution0
Scale-wise Convolution for Image RestorationCode1
Adaptive Densely Connected Super-Resolution ReconstructionCode0
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
Image Processing Using Multi-Code GAN PriorCode0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features0
Explorable Super ResolutionCode0
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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