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 1421–1430 of 3874 papers

TitleStatusHype
Efficient Denoising Method to Improve The Resolution of Satellite Images—0
SuperResolution Radar Gesture Recognitio—0
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation—0
A Modular Conditional Diffusion Framework for Image Reconstruction—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
Electro-diffusive modeling and the role of spine geometry on action potential propagation in neurons—0
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing—0
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-ResolutionCode0
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision AgricultureCode0
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified