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 2826–2850 of 3874 papers

TitleStatusHype
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
A learning-based view extrapolation method for axial super-resolution—0
Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Deep learning-based super-resolution fluorescence microscopy on small datasetsCode0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Super-Resolution DOA Estimation for Wideband Signals using Arbitrary Linear Arrays without Focusing Matrices—0
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Deep Unrolled Network for Video Super-Resolution—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Mobile Computational Photography: A Tour—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified