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 2161–2170 of 3874 papers

TitleStatusHype
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
LSR: A Light-Weight Super-Resolution Method—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
DISCO: Distributed Inference with Sparse Communications—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified