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 1681–1690 of 3874 papers

TitleStatusHype
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Federated Learning for Blind Image Super-Resolution—0
HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
Feature Super-Resolution: Make Machine See More Clearly—0
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection—0
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified