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 1111–1120 of 3874 papers

TitleStatusHype
An End-Cloud Computing Enabled Surveillance Video Transmission System—0
Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data—0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
Learning-Based and Quality Preserving Super-Resolution of Noisy Images—0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation—0
Exploring Deep Learning Image Super-Resolution for Iris Recognition—0
Optimal Transport-Guided Conditional Score-Based Diffusion ModelsCode1
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified