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 491–500 of 3874 papers

TitleStatusHype
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Image Restoration Through Generalized Ornstein-Uhlenbeck BridgeCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
Image super-resolution via dynamic networkCode1
Image Super-resolution with An Enhanced Group Convolutional Neural NetworkCode1
Deep Blind Super-Resolution for Satellite VideoCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified