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 3076–3100 of 3874 papers

TitleStatusHype
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal—0
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network—0
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
A Novel Dual Dense Connection Network for Video Super-resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Signal reconstruction via operator guiding—0
SIGNET: Efficient Neural Representation for Light Fields—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models—0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction—0
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics—0
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities—0
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network—0
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding—0
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified