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 3661–3670 of 3874 papers

TitleStatusHype
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge—0
Fingerprinting Deep Image Restoration Models—0
Fingerprints of Super Resolution Networks—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
First order algorithms in variational image processing—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Advancements in Image Resolution: Super-Resolution Algorithm for Enhanced EOS-06 OCM-3 Data—0
Efficient Multi-disparity Transformer for Light Field Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified