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 931–940 of 3874 papers

TitleStatusHype
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
Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding schemeCode1
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
Hierarchical Prior-based Super Resolution for Point Cloud Geometry CompressionCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified