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 2501–2510 of 3874 papers

TitleStatusHype
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
Optical Flow for Video Super-Resolution: A Survey—0
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Image Super-Resolution With Deep Variational Autoencoders—0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified