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 2571–2580 of 3874 papers

TitleStatusHype
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-ResolutionCode1
Mathematical Theory of Computational Resolution Limit in Multi-dimensions—0
Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentationCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified