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 3031–3040 of 3874 papers

TitleStatusHype
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Deep Learning for Cornea Microscopy Blind DeblurringCode0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling—0
Progressively Unfreezing Perceptual GAN—0
What's in the Image? Explorable Decoding of Compressed Images—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified