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 3801–3810 of 3874 papers

TitleStatusHype
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Global graph features unveiled by unsupervised geometric deep learning—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Global-Local Face Upsampling Network—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified