SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3801–3825 of 3874 papers

TitleStatusHype
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Flexible Alignment Super-Resolution Network for Multi-Contrast MRICode0
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial ImageryCode0
Deep Learning-Based Channel EstimationCode0
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
Calorimeter shower superresolutionCode0
Continual Learning Approaches for Anomaly DetectionCode0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
A Fusion-Guided Inception Network for Hyperspectral Image Super-ResolutionCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Super-Efficient Super Resolution for Fast Adversarial Defense at the EdgeCode0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Fine-grained Urban Flow Inference with Incomplete DataCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From ShadingCode0
Sampling Theory for Super-Resolution with Implicit Neural RepresentationsCode0
SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationCode0
Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition KernelsCode0
A Fully Progressive Approach to Single-Image Super-ResolutionCode0
Deep Laplacian Pyramid Networks for Fast and Accurate Super-ResolutionCode0
Two-Stream Action Recognition-Oriented Video Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified