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 2661–2670 of 3874 papers

TitleStatusHype
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
Structured Bayesian Gaussian process latent variable model—0
Structured illumination microscopy for dual-modality 3D sub-diffraction resolution fluorescence and refractive-index reconstruction—0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Structure Tensor Based Image Interpolation Method—0
Studying Very Low Resolution Recognition Using Deep Networks—0
Study of Subjective and Objective Quality in Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset—0
Exploiting Style and Attention in Real-World Super-Resolution—0
StyleDEM: a Versatile Model for Authoring Terrains—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified