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 3451–3460 of 3874 papers

TitleStatusHype
Dense xUnit NetworksCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
Low-Resolution Face Recognition—0
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound—0
Adversarial Feedback LoopCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified