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 1001–1050 of 1589 papers

TitleStatusHype
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Deep learning architectural designs for super-resolution of noisy imagesCode1
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity AttackCode1
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Exploiting Raw Images for Real-Scene Super-ResolutionCode1
Deep Burst Super-ResolutionCode1
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Progressive Image Super-Resolution via Neural Differential Equation—0
GhostSR: Learning Ghost Features for Efficient Image Super-ResolutionCode0
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
Deep Learning-based Face Super-Resolution: A SurveyCode1
Single Image Super-Resolution—0
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination—0
Dual-Stream Fusion Network for Spatiotemporal Video Super-ResolutionCode0
Transformers in Vision: A Survey—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Context Reasoning Attention Network for Image Super-Resolution—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGANCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
Attention-based Image Upsampling—0
Neural Radiance Flow for 4D View Synthesis and Video ProcessingCode1
Learning-Based Quality Assessment for Image Super-Resolution—0
Learning Continuous Image Representation with Local Implicit Image FunctionCode2
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Learning Spatial Attention for Face Super-ResolutionCode1
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Pre-Trained Image Processing TransformerCode1
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Fully Quantized Image Super-Resolution NetworksCode1
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Rank-One Network: An Effective Framework for Image RestorationCode1
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—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