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 2491–2500 of 3874 papers

TitleStatusHype
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Anchor-based Plain Net for Mobile Image Super-ResolutionCode1
Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration NetworkCode1
XCycles Backprojection Acoustic Super-Resolution—0
Automated Symbolic Law Discovery: A Computer Vision Approach—0
Improved detection of small objects in road network sequences—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report—0
Parallel and Flexible Sampling from Autoregressive Models via Langevin DynamicsCode1
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: ReportCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified