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 2261–2270 of 3874 papers

TitleStatusHype
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
Mesoscopic Facial Geometry Inference Using Deep Neural Networks—0
Metadata-Based RAW Reconstruction via Implicit Neural Functions—0
Convolutional Low-Resolution Fine-Grained Classification—0
Convolutional Bipartite Attractor Networks—0
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
Controlling Neural Networks via Energy Dissipation—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified