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 3201–3210 of 3874 papers

TitleStatusHype
Training Set Effect on Super Resolution for Automated Target Recognition—0
Facial Expression Restoration Based on Improved Graph Convolutional Networks—0
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach—0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Component Attention Guided Face Super-Resolution Network: CAGFaceCode0
Multimodal Image Super-resolution via Deep Unfolding with Side Information—0
Image Deconvolution with Deep Image and Kernel Priors—0
Translation position extracting in incoherent Fourier ptychography—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified