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 1741–1750 of 3874 papers

TitleStatusHype
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
TITAN: Bringing The Deep Image Prior to Implicit RepresentationsCode0
Self-supervised Character-to-Character Distillation for Text RecognitionCode1
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical SystemsCode0
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Data-Driven Computational Imaging for Scientific DiscoveryCode0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified