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 321–330 of 3874 papers

TitleStatusHype
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement TasksCode1
Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution NetworkCode1
Simple Base Frame Guided Residual Network for RAW Burst Image Super-ResolutionCode1
Adaptive Convolutional Neural Network for Image Super-resolutionCode1
Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding schemeCode1
Hierarchical Prior-based Super Resolution for Point Cloud Geometry CompressionCode1
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion modelsCode1
LKFormer: Large Kernel Transformer for Infrared Image Super-ResolutionCode1
The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and AggregationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified