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 1231–1240 of 3874 papers

TitleStatusHype
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
StofNet: Super-resolution Time of Flight NetworkCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Calorimeter shower superresolutionCode0
Towards Clip-Free Quantized Super-Resolution Networks: How to Tame Representative Images—0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy—0
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified