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 1621–1630 of 3874 papers

TitleStatusHype
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-ResolutionCode0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Distortion-aware super-resolution for planetary exploration imagesCode0
High-Quality Face Image SR Using Conditional Generative Adversarial NetworksCode0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Brain MRI super-resolution using 3D generative adversarial networksCode0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-ResolutionCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified