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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2991–3000 of 3874 papers

TitleStatusHype
Cascade Convolutional Neural Network for Image Super-Resolution—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
Deep Photo Cropper and Enhancer—0
Binarized Neural Network for Single Image Super Resolution—0
Show:102550
← PrevPage 300 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified