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 671–680 of 3874 papers

TitleStatusHype
Neuromorphic Imaging with Super-Resolution—0
Deform-Mamba Network for MRI Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis—0
A Hybrid Registration and Fusion Method for Hyperspectral Super-resolution—0
Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-ResolutionCode0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified