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 2891–2900 of 3874 papers

TitleStatusHype
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
TriNeRFLet: A Wavelet Based Triplane NeRF Representation—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Trustworthy modelling of atmospheric formaldehyde powered by deep learning—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond—0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks—0
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified