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 3791–3800 of 3874 papers

TitleStatusHype
Matrix Neural Networks—0
Matrix Variate RBM and Its Applications—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Face Hallucination using Linear Models of Coupled Sparse Support—0
Double Sparse Multi-Frame Image Super Resolution—0
Example-Based Modeling of Facial Texture From Deficient Data—0
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Convolutional Sparse Coding for Image Super-Resolution—0
Naive Bayes Super-Resolution Forest—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified