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 9761000 of 1589 papers

TitleStatusHype
Attention in Attention Network for Image Super-ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging0
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Training a Task-Specific Image Reconstruction Loss0
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
Asymmetric CNN for image super-resolutionCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network0
A new public Alsat-2B dataset for single-image super-resolutionCode1
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings0
Generic Perceptual Loss for Modeling Structured Output Dependencies0
Self-Supervised Adaptation for Video Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
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
Real-World Single Image Super-Resolution: A Brief ReviewCode1
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution EnhancementCode1
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution0
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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