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 12011250 of 1589 papers

TitleStatusHype
RefQSR: Reference-based Quantization for Image Super-Resolution Networks0
Image restoration quality assessment based on regional differential information entropy0
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks0
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art0
Characteristic Regularisation for Super-Resolving Face Images0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO0
Residual Feature Aggregation Network for Image Super-Resolution0
Channel Splitting Network for Single MR Image Super-Resolution0
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution0
Residual Networks for Light Field Image Super-Resolution0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution0
Unsupervised Degradation Learning for Single Image Super-Resolution0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution0
CasSR: Activating Image Power for Real-World Image Super-Resolution0
Rethinking Image Evaluation in Super-Resolution0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective0
Rethinking Super-Resolution as Text-Guided Details Generation0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution0
Cascade Convolutional Neural Network for Image Super-Resolution0
Unsupervised Image Noise Modeling with Self-Consistent GAN0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales0
RGB-Guided Resolution Enhancement of IR Images0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns0
Burst Image Super-Resolution with Mamba0
Burst Image Super-Resolution with Base Frame Selection0
Robust Regression via Deep Negative Correlation Learning0
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior0
Robust Unpaired Single Image Super-Resolution of Faces0
Rolling Shutter Super-Resolution0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection0
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis0
BSRAW: Improving Blind RAW Image Super-Resolution0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models0
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation0
Boosting Optical Character Recognition: A Super-Resolution Approach0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios0
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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