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 751–760 of 3874 papers

TitleStatusHype
Joint Implicit Image Function for Guided Depth Super-ResolutionCode1
Real-Time Super-Resolution System of 4K-Video Based on Deep LearningCode1
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and KernelCode1
On Measuring and Controlling the Spectral Bias of the Deep Image PriorCode1
Text Prior Guided Scene Text Image Super-resolutionCode1
Multi-Modal Transformer for Accelerated MR ImagingCode1
Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation ModelCode1
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified