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 1921–1930 of 3874 papers

TitleStatusHype
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing—0
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization—0
Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data—0
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling—0
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified