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

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Showing 1201–1250 of 1589 papers

TitleStatusHype
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Image restoration quality assessment based on regional differential information entropy—0
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks—0
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art—0
Characteristic Regularisation for Super-Resolving Face Images—0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
Residual Feature Aggregation Network for Image Super-Resolution—0
Channel Splitting Network for Single MR Image Super-Resolution—0
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution—0
Residual Networks for Light Field Image Super-Resolution—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Rethinking Image Evaluation in Super-Resolution—0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales—0
RGB-Guided Resolution Enhancement of IR Images—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
Burst Image Super-Resolution with Mamba—0
Burst Image Super-Resolution with Base Frame Selection—0
Robust Regression via Deep Negative Correlation Learning—0
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
Robust Unpaired Single Image Super-Resolution of Faces—0
Rolling Shutter Super-Resolution—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
BSRAW: Improving Blind RAW Image Super-Resolution—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified