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 2076–2100 of 3874 papers

TitleStatusHype
SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect ArrayCode1
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolutionCode1
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Image Super-Resolution With Deep Variational Autoencoders—0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face RestorationCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-ResolutionCode1
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Enriched CNN-Transformer Feature Aggregation Networks for Super-ResolutionCode1
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
STDAN: Deformable Attention Network for Space-Time Video Super-ResolutionCode1
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Rethinking data-driven point spread function modeling with a differentiable optical modelCode1
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified