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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1691–1700 of 3874 papers

TitleStatusHype
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
Federated Learning for Blind Image Super-Resolution—0
Feature Super-Resolution: Make Machine See More Clearly—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
Incorporating Degradation Estimation in Light Field Spatial Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified