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 2191–2200 of 3874 papers

TitleStatusHype
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Low-Resolution Action Recognition for Tiny Actions Challenge—0
Low-Resolution Face Recognition—0
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques—0
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture—0
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification—0
Low Resource Video Super-resolution using Memory and Residual Deformable Convolutions—0
Training Set Effect on Super Resolution for Automated Target Recognition—0
LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network—0
LSR: A Light-Weight Super-Resolution Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified