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 2881–2890 of 3874 papers

TitleStatusHype
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches—0
Transformer-Driven Inverse Problem Transform for Fast Blind Hyperspectral Image Dehazing—0
Transformers in Vision: A Survey—0
Translation-based Video-to-Video Synthesis—0
Translation position extracting in incoherent Fourier ptychography—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified