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 2241–2250 of 3874 papers

TitleStatusHype
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical modelCode1
Investigating Tradeoffs in Real-World Video Super-ResolutionCode2
Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—0
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified