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 1661–1670 of 3874 papers

TitleStatusHype
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model—0
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified