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 2001–2010 of 3874 papers

TitleStatusHype
DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar—0
Do Deepfake Detectors Work in Reality?—0
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation—0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Perceptual Image Super-Resolution with Progressive Adversarial Network—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
Double Sparse Multi-Frame Image Super Resolution—0
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified