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 1191–1200 of 3874 papers

TitleStatusHype
GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRICode0
DSSR-Net for Super-Resolution Radar Range Profiles—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution—0
Representing Flow Fields with Divergence-Free Kernels for Reconstruction—0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model ApproachCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial ImageryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified