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 2701–2725 of 3874 papers

TitleStatusHype
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Super-resolution data assimilation—0
Super-Resolution DOA Estimation for Wideband Signals using Arbitrary Linear Arrays without Focusing Matrices—0
Super-resolution estimation of cyclic arrival rates—0
Super-Resolution Estimation of UWB Channels including the Diffuse Component -- An SBL-Inspired Approach—0
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability—0
Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis—0
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning—0
Super-Resolution for Practical Automated Plant Disease Diagnosis System—0
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning—0
Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine—0
Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris—0
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Super-Resolution Harmonic Retrieval of Non-Circular Signals—0
Super-resolution image display using diffractive decoders—0
Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN—0
Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach—0
Super-resolution imaging using super-oscillatory diffractive neural networks—0
Super-resolution in disordered media using neural networks—0
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks—0
Super-resolution meets machine learning: approximation of measures—0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Superresolution method for data deconvolution by superposition of point sources—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified