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 351375 of 1589 papers

TitleStatusHype
Learnable Lookup Table for Neural Network QuantizationCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
Reflash Dropout in Image Super-ResolutionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
On Efficient Transformer-Based Image Pre-training for Low-Level VisionCode1
Feature Distillation Interaction Weighting Network for Lightweight Image Super-ResolutionCode1
Text Gestalt: Stroke-Aware Scene Text Image Super-ResolutionCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-ResolutionCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Local Texture Estimator for Implicit Representation FunctionCode1
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
Pansharpening by convolutional neural networks in the full resolution frameworkCode1
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural RepresentationCode1
Improving Super-Resolution Performance using Meta-Attention LayersCode1
ERQA: Edge-Restoration Quality Assessment for Video Super-ResolutionCode1
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale DatasetCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Structure-Preserving Image Super-ResolutionCode1
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Transformer for Single Image Super-ResolutionCode1
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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