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 3031–3040 of 3874 papers

TitleStatusHype
Neural Operators for Accelerating Scientific Simulations and Design—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Neural Prior for Trajectory Estimation—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Neural Volume Super-Resolution—0
Neuromorphic Imaging with Super-Resolution—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
Neutron Ghost Imaging—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
New wavelet-based superresolution algorithm for speckle reduction in SAR images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified