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 1976–2000 of 3874 papers

TitleStatusHype
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video RestorationCode1
Combining Contrastive and Supervised Learning for Video Super-Resolution DetectionCode0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Residual Local Feature Network for Efficient Super-ResolutionCode1
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array—0
Evaluating the Generalization Ability of Super-Resolution Networks—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRICode0
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and ResultsCode1
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
Joint OAM Radar-Communication Systems: Target Recognition and Beam Optimization—0
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch NormalizationCode0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
Accelerating the Training of Video Super-Resolution ModelsCode0
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-ResolutionCode1
Activating More Pixels in Image Super-Resolution TransformerCode3
A Real Time Super Resolution Accelerator with Tilted Layer Fusion—0
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images—0
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-ResolutionCode1
Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified