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 1251–1275 of 3874 papers

TitleStatusHype
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
Frame-Recurrent Video Super-Resolution—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Deep learning in ultrasound imaging—0
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data—0
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
Frame and Feature-Context Video Super-Resolution—0
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified