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 3091–3100 of 3874 papers

TitleStatusHype
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
Show:102550
← PrevPage 310 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified