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 3351–3360 of 3874 papers

TitleStatusHype
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution—0
Residual Non-local Attention Networks for Image RestorationCode0
Feedback Network for Image Super-ResolutionCode0
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution NetworkCode0
A Matrix-in-matrix Neural Network for Image Super ResolutionCode0
Proximal Splitting Networks for Image Restoration—0
Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples—0
Robust Super-Resolution GAN, with Manifold-based and Perception Loss—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified