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 1101–1110 of 3874 papers

TitleStatusHype
A Survey of Deep Learning Video Super-Resolution—0
Application of convolutional neural networks in image super-resolution—0
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance—0
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language ModelCode0
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey—0
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface—0
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified