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 2271–2280 of 3874 papers

TitleStatusHype
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Super-resolution Reconstruction of Single Image for Latent features—0
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution—0
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Two-dimensional gridless super-resolution method for ISAR imaging—0
A Comprehensive Survey of Transformers for Computer Vision—0
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified