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 2691–2700 of 3874 papers

TitleStatusHype
Predicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
Prediction-assistant Frame Super-Resolution for Video Streaming—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Blind Image Super-Resolution: A Survey and Beyond—0
Prior Knowledge Distillation Network for Face Super-Resolution—0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis—0
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified