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 11011125 of 1589 papers

TitleStatusHype
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding0
High-Frequency aware Perceptual Image Enhancement0
Content-adaptive Representation Learning for Fast Image Super-resolution0
XCycles Backprojection Acoustic Super-Resolution0
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution0
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial NetworksCode0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution0
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic IndexesCode0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS0
Kernel Adversarial Learning for Real-world Image Super-resolution0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging0
Training a Task-Specific Image Reconstruction Loss0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings0
Generic Perceptual Loss for Modeling Structured Output Dependencies0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution0
Selfie Periocular Verification using an Efficient Super-Resolution Approach0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
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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