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 3571–3580 of 3874 papers

TitleStatusHype
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
Sparsity-based background removal for STORM super-resolution imagesCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Joint High Dynamic Range Imaging and Super-Resolution from a Single ImageCode0
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRICode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified