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 3751–3760 of 3874 papers

TitleStatusHype
Hierarchical Back Projection Network for Image Super-ResolutionCode0
HF-Diff: High-Frequency Perceptual Loss and Distribution Matching for One-Step Diffusion-Based Image Super-ResolutionCode0
HASN: Hybrid Attention Separable Network for Efficient Image Super-resolutionCode0
Harnessing Multi-resolution and Multi-scale Attention for Underwater Image RestorationCode0
Deep learning for temporal super-resolution 4D Flow MRICode0
Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-ResolutionCode0
Deep Learning for Single Image Super-Resolution: A Brief ReviewCode0
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.Code0
Grids Often Outperform Implicit Neural RepresentationsCode0
AIM 2019 Challenge on Constrained Super-Resolution: Methods and ResultsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified