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 421–430 of 3874 papers

TitleStatusHype
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine LearningCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Minimalist and High-Quality Panoramic Imaging with PSF-aware TransformersCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual DataCode1
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-ResolutionCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified