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 3491–3500 of 3874 papers

TitleStatusHype
Physics-Informed Ensemble Representation for Light-Field Image Super-ResolutionCode0
Towards Representation Learning for Atmospheric DynamicsCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-ResolutionCode0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Towards Super-Resolution CEST MRI for Visualization of Small StructuresCode0
Edge-Informed Single Image Super-ResolutionCode0
Super-Resolution with Deep Convolutional Sufficient StatisticsCode0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified