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 1431–1440 of 3874 papers

TitleStatusHype
CT-image Super Resolution Using 3D Convolutional Neural Network—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
A Survey on Deep learning based Document Image Enhancement—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
Show:102550
← PrevPage 144 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified