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 2381–2390 of 3874 papers

TitleStatusHype
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
Hyperspectral Neural Radiance Fields—0
Hyperspectral Spatial Super-Resolution using Keystone Error—0
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach—0
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach—0
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling—0
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified