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 2751–2775 of 3874 papers

TitleStatusHype
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network—0
Super-Resolution Reconstruction of Electrical Impedance Tomography Images—0
Super-Resolution Reconstruction of Interval Energy Data—0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
Super-resolution Reconstruction of Single Image for Latent features—0
Super-Resolution Remote Imaging using Time Encoded Remote Apertures—0
Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off—0
Super-resolution Ultrasound Localization Microscopy through Deep Learning—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Super Resolution Using Segmentation-Prior Self-Attention Generative Adversarial Network—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Super-Resolution via Deep Learning—0
Super-Resolution via Learned Predictor—0
Super-resolution Wideband Beam Training for Near-field Communications with Ultra-low Overhead—0
Super-resolution with Binary Priors: Theory and Algorithms—0
Super-Resolution with Deep Adaptive Image Resampling—0
Super-resolution with Sparse Arrays: A Non-Asymptotic Analysis of Spatio-temporal Trade-offs—0
Super-Resolution with Structured Motion—0
Super-Resolution works for coastal simulations—0
Super-resolved Localisation without Identifying LoS/NLoS Paths—0
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
Super-resolved virtual staining of label-free tissue using diffusion models—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified