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 2671–2680 of 3874 papers

TitleStatusHype
Plasma Surrogate Modelling using Fourier Neural Operators—0
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications—0
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Blind Restoration of High-Resolution Ultrasound Video—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified