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

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Showing 1421–1430 of 3874 papers

TitleStatusHype
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
Enhancing Video Super-Resolution via Implicit Resampling-based AlignmentCode2
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
GlyphDiffusion: Text Generation as Image Generation—0
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging—0
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified