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 1591–1600 of 3874 papers

TitleStatusHype
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution—0
Deform-Mamba Network for MRI Super-Resolution—0
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis—0
Neuromorphic Imaging with Super-Resolution—0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified