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 1891–1900 of 3874 papers

TitleStatusHype
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution—0
IFF: A Super-resolution Algorithm for Multiple Measurements—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified