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 2121–2130 of 3874 papers

TitleStatusHype
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Disentangling Light Fields for Super-Resolution and Disparity Estimation—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation—0
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks—0
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
Single Image Super-Resolution Methods: A Survey—0
Mapping molecular complexes with Super-Resolution Microscopy and Single-Particle Analysis—0
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified