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 1101–1150 of 1589 papers

TitleStatusHype
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding—0
High-Frequency aware Perceptual Image Enhancement—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
XCycles Backprojection Acoustic Super-Resolution—0
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution—0
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial NetworksCode0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic IndexesCode0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Training a Task-Specific Image Reconstruction Loss—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
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
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
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
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
Progressive Image Super-Resolution via Neural Differential Equation—0
GhostSR: Learning Ghost Features for Efficient Image Super-ResolutionCode0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
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
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Context Reasoning Attention Network for Image Super-Resolution—0
Attention-based Image Upsampling—0
Learning-Based Quality Assessment for Image Super-Resolution—0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—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