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 611–620 of 3874 papers

TitleStatusHype
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion ModuleCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
Collaborative Feedback Discriminative Propagation for Video Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified