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 2461–2470 of 3874 papers

TitleStatusHype
Neuromorphic Imaging with Super-Resolution—0
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
Neutron Ghost Imaging—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
New wavelet-based superresolution algorithm for speckle reduction in SAR images—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified