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 1881–1890 of 3874 papers

TitleStatusHype
Deep Learning Techniques for Super-Resolution in Video Games—0
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video—0
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images—0
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image 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
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified