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 2931–2940 of 3874 papers

TitleStatusHype
Super-Resolution of Real-World Faces—0
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models—0
Generating Unobserved Alternatives—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Wavelet Flow: Fast Training of High Resolution Normalizing FlowsCode0
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified