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 2671–2680 of 3874 papers

TitleStatusHype
Application of Video-to-Video Translation Networks to Computational Fluid Dynamics—0
Toward Real-World Super-Resolution via Adaptive Downsampling Models—0
Mid-wave infrared super-resolution imaging based on compressive calibration and sampling—0
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks—0
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds—0
FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
Super-resolution data assimilation—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified