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 2831–2840 of 3874 papers

TitleStatusHype
Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Deep learning-based super-resolution fluorescence microscopy on small datasetsCode0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Super-Resolution DOA Estimation for Wideband Signals using Arbitrary Linear Arrays without Focusing Matrices—0
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Deep Unrolled Network for Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified