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 2891–2900 of 3874 papers

TitleStatusHype
A deep primal-dual proximal network for image restoration—0
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANsCode0
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
Deep Learning for Cornea Microscopy Blind DeblurringCode0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
Show:102550
← PrevPage 290 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified