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 1651–1660 of 3874 papers

TitleStatusHype
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
Anatomically Guided Motion Correction for Placental IVIM Parameter Estimation with Accelerated Sampling Method—0
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
Diffusion Image Prior—0
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data—0
Diffusion-based Light Field Synthesis—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images—0
Boomerang: Local sampling on image manifolds using diffusion models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified