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 3261–3270 of 3874 papers

TitleStatusHype
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Deep learning in ultrasound imaging—0
Distilling with Residual Network for Single Image Super Resolution—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix EstimationCode0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
CNN-based synthesis of realistic high-resolution LiDAR data—0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified