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 2751–2760 of 3874 papers

TitleStatusHype
Super-resolution of periodic signals from short sequences of samples—0
Non-convex Super-resolution of OCT images via sparse representation—0
Face Hallucination via Split-Attention in Split-Attention NetworkCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset—0
Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation—0
Fourier Neural Operator for Parametric Partial Differential EquationsCode2
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
Light Stage Super-Resolution: Continuous High-Frequency Relighting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified