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 2451–2460 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
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Combining Transformer Generators with Convolutional Discriminators—0
Combined Generative and Predictive Modeling for Speech Super-resolution—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
Neural Volume Super-Resolution—0
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified