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 2531–2540 of 3874 papers

TitleStatusHype
One Target, Many Views: Multi-User Fusion for Collaborative Uplink ISAC—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements—0
Online Streaming Video Super-Resolution with Convolutional Look-Up Table—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance—0
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion—0
Cascaded Diffusion Models for High Fidelity Image Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified