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 271–280 of 3874 papers

TitleStatusHype
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified