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 351–360 of 3874 papers

TitleStatusHype
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Hazy Low-Quality Satellite Video Restoration Via Learning Optimal Joint Degradation Patterns and Continuous-Scale Super-Resolution Reconstruction—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)Code1
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—0
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement—0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified