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 1431–1440 of 3874 papers

TitleStatusHype
Super-Resolution without High-Resolution Labels for Black Hole SimulationsCode0
Constrained Diffusion Implicit Models—0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels—0
Enhancing Image Resolution: A Simulation Study and Sensitivity Analysis of System Parameters for Resourcesat-3S/3SA—0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images—0
Fingerprints of Super Resolution Networks—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
Super-resolution in disordered media using neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified