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 12261250 of 1589 papers

TitleStatusHype
Across-scale Process Similarity based Interpolation for Image Super-Resolution0
High-Resolution Daytime Translation Without Domain LabelsCode0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks0
Perceptual Image Super-Resolution with Progressive Adversarial Network0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data0
Generator From Edges: Reconstruction of Facial Images0
Facial Attribute Capsules for Noise Face Super Resolution0
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
Multimodal Deep Unfolding for Guided Image Super-Resolution0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution0
Learned Multi-View Texture Super-Resolution0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES0
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
Adaptive Densely Connected Super-Resolution ReconstructionCode0
Image Processing Using Multi-Code GAN PriorCode0
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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