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 3381–3390 of 3874 papers

TitleStatusHype
Deep learning Framework for Mobile MicroscopyCode0
MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-MixerCode0
On the Diversity of Realistic Image SynthesisCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Super-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation AccuracyCode0
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified