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 1441–1450 of 3874 papers

TitleStatusHype
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts—0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution—0
A survey of machine learning-based physics event generation—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified