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 3621–3630 of 3874 papers

TitleStatusHype
Video Enhancement with Task-Oriented FlowCode1
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
The Perception-Distortion TradeoffCode0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
Single Image Super-Resolution Using Lightweight CNN with Maxout Units—0
Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization—0
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network—0
Remote Sensing Image Fusion Based on Two-stream Fusion NetworkCode0
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach—0
Show:102550
← PrevPage 363 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified