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 3431–3440 of 3874 papers

TitleStatusHype
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning PriorCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Universally Slimmable Networks and Improved Training TechniquesCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
VibrantLeaves: A principled parametric image generator for training deep restoration modelsCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Show:102550
← PrevPage 344 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified