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 2131–2140 of 3874 papers

TitleStatusHype
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRICode0
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in ImagingCode1
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
Accurate super-resolution low-field brain MRI—0
Patch-Based Stochastic Attention for Image EditingCode0
A new face swap method for image and video domains: a technical reportCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified