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 2351–2360 of 3874 papers

TitleStatusHype
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Realistic Hair Synthesis with Generative Adversarial Networks—0
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality AssessmentCode0
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—0
Magnitude-image based data-consistent deep learning method for MRI super resolution—0
Video Restoration with a Deep Plug-and-Play Prior—0
Deep filter bank regression for super-resolution of anisotropic MR brain images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified