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 1391–1400 of 3874 papers

TitleStatusHype
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Screentone-Aware Manga Super-Resolution Using DeepLearning—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening—0
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution—0
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect—0
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Can SAM Boost Video Super-Resolution?—0
Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility—0
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified