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 1371–1380 of 3874 papers

TitleStatusHype
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth—0
A Two-Stage Rotation-Based Super-Resolution Signature Estimation for Spatial Wideband Systems—0
MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
A Generative Model for Generic Light Field Reconstruction—0
Generator From Edges: Reconstruction of Facial Images—0
Deconvolution with a Box—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified