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 2751–2800 of 3874 papers

TitleStatusHype
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network—0
Super-Resolution Reconstruction of Electrical Impedance Tomography Images—0
Super-Resolution Reconstruction of Interval Energy Data—0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
Super-resolution Reconstruction of Single Image for Latent features—0
Super-Resolution Remote Imaging using Time Encoded Remote Apertures—0
Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off—0
Super-resolution Ultrasound Localization Microscopy through Deep Learning—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Super Resolution Using Segmentation-Prior Self-Attention Generative Adversarial Network—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Super-Resolution via Deep Learning—0
Super-Resolution via Learned Predictor—0
Super-resolution Wideband Beam Training for Near-field Communications with Ultra-low Overhead—0
Super-resolution with Binary Priors: Theory and Algorithms—0
Super-Resolution with Deep Adaptive Image Resampling—0
Super-resolution with Sparse Arrays: A Non-Asymptotic Analysis of Spatio-temporal Trade-offs—0
Super-Resolution with Structured Motion—0
Super-Resolution works for coastal simulations—0
Super-resolved Localisation without Identifying LoS/NLoS Paths—0
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
Super-resolved virtual staining of label-free tissue using diffusion models—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images—0
Super-Resolving Blurry Images with Events—0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar—0
Super-Resolving Noisy Images—0
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach—0
Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes—0
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time—0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation—0
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface—0
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Surveillance Face Anti-spoofing—0
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting—0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks—0
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified