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 1971–1980 of 3874 papers

TitleStatusHype
Calorimeter shower superresolutionCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy—0
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations—0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
CMISR: Circular Medical Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified