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 1751–1775 of 3874 papers

TitleStatusHype
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Thermodynamics-informed super-resolution of scarce temporal dynamics data—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings—0
XAI-based gait analysis of patients walking with Knee-Ankle-Foot orthosis using video cameras—0
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing DataCode0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Scene Prior Filtering for Depth Super-Resolution—0
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion—0
Diffusion Posterior Sampling is Computationally Intractable—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep modelCode0
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results—0
Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO—0
Arbitrary Scale Super-Resolution Assisted Lunar Crater Detection in Satellite Images—0
3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN—0
Hybrid Neural Representations for Spherical Data—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
Diffusion-based Light Field Synthesis—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified