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 521–530 of 3874 papers

TitleStatusHype
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Deep Image PriorCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Deep Burst Super-ResolutionCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse ArraysCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified