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 1571–1580 of 3874 papers

TitleStatusHype
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution—0
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
G-PCC Post-Processing Using Fractional Super-Resolution—0
Gradient-Free Adversarial Purification with Diffusion Models—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified