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 1531–1540 of 3874 papers

TitleStatusHype
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Image Super-Resolution via Attention based Back Projection NetworksCode0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Image Super-Resolution by Neural Texture TransferCode0
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancementCode0
Image Super-Resolution Improved by Edge InformationCode0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified