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 1721–1730 of 3874 papers

TitleStatusHype
Interpretable Deep Multimodal Image Super-Resolution—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
A full-resolution training framework for Sentinel-2 image fusion—0
Hyperspectral Neural Radiance Fields—0
Convolutional Sparse Coding for Image Super-Resolution—0
Hyperspectral Spatial Super-Resolution using Keystone Error—0
Integrated Super-Resolution Sensing and Communication with 5G NR Waveform: Signal Processing with Uneven CPs and Experiments—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified